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Factors affecting students’ career choice of science and engineering Wang, Zhen 1995

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FACTORS AFFECTING STUDENTS CAREER CHOICE OF SCIENCE AND ENGINEERING by ZHEN WANG .A. The Shanghai University of Science and Technology, 1988 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTERS OF ARTS in THE FACULTY OF GRADUATE STUDIES (DEPARTMENT OF CURRICULUM STUDIES) We Accept This Thesis As Conforming To The Required Standard THE UNIVERSITY OF BRITISH COLUMBIA April 1995 © ZHEN WANG, 1995 In presenting t h i s thesis i n p a r t i a l f u l f i l l m e n t of the requirements for an advanced degree at the University of B r i t i s h Columbia/ I agree that the Library s h a l l make i t f r e e l y available for reference and study. I further agree that permission for extensive copying of t h i s thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It i s understood that copying or publication of t h i s thesis for f i n a n c i a l gain s h a l l not be allowed without my written permission. j Department of The University of B r i t i s h Columbia Vancouver/ Canada 11 ABSTRACT This study attempts to examine the potential differences that exist between male and female students, and between science career choosers and non-science career choosers in terms of factors that were thought to influence their academic and career decision making. Four types of factors are considered: 1) Mathematics and science background; 2) Attitudes toward school science activity; 3) Perceptions of various influences on career choice; 4) Perceptions of their own personality. A survey was carried out in British Columbia between February and June, 1994. The subjects were 316 Grade 12 students randomly drawn from 16 districts (20 schools), and they are considered to be representative of the British Columbia school population. Data for the study were collected by using a previously developed questionnaire (Woolnough, 1991) which was modified by the researcher. The questionnaire was administed in each school by the school counselor. The data were then analyzed by using SPSS Mainframe. The major findings of the study were: 1) Fewer female students selected physical-science related careers in comparison with male students; 2) Science career choosers selected more mathematics and science courses than students selecting a non-science career; the Ill mathematics and science G P A of non-science career choosers was significantly lower than that of science career choosers; 3) Science career choosers thought that "family", "school success", "out-of-school science", and "personality" were more encouraging for them to enter science and engineering careers than did non-science choosers; 4) Male students thought that "family", "school success", and "personality" were more encouraging for them to enter science and engineering careers than did female students. The findings suggest that the important factors that affect students' choice of a career in science and engineering can be: family; school success; school science; out-of-school science; and personality. Suggestions are made for further practice and research to address the issues and concerns raised by the study. IV T A B L E O F C O N T E N T S Abstract ii Table of Contents iv List of Tables vi Acknowledgement v i i i Chapters One: Introduction 1 I. Background 1 II. Focus of the Study 4 III. Specific Research Questions 5 I V . Significance of the Study 6 V . Limitation of the Study 6 Two: Literature Review 8 I. Introduction 8 II. School Science Background (Course Participation). . . . 8 a. science achievement 10 b. interest in the subject 11 c. perceived ability 12 d. prerequisites 13 e. teacher 14 III. Students' Attitudes Toward School Science 15 I V . Perceptions of the factors that influence students career choice IV a. job characteristics 18 b. family influence 19 c. family responsibility 2 0 V . Students' Perceptions of their own Personality 2 1 V I . Summary 2 2 Three: Methodology of the Study 2 4 I. Introduction 2 4 II. The Sample 2 4 III. Return of Completed Questionnaires 2 5 I V . Instrumentation 2 5 V . University and District Approval 3 0 V VI. Statistical Analysis i . . . 3 0 Four: Results 3 5 I. Introduction 3 5 II. Gender and Career Choice 3 6 III. Course Participation and Career Choice 3 7 IV. Student Achievement and Career Choice 4 0 V. Career Choice and Parents' Occupation. 4 2 VI Students' Reasons For Course Selection. 46 VII. Students' Attitude Toward School Science Activity. . . 4 9 VIII Students' Perceptions of the Factors that Affect their Choice of Career 5 9 IX. Students' Perceptions of their Own Personality 6 9 Five: Discussion and Implications 7 7 I. Introduction 7 7 II. Discussions 7 7 a. gender and career choice 7 7 b science background 7 8 c. attitudes toward school science activity 8 0 e. students' perceptions of their own personality . . . . 8 4 III. Recommendations and Suggestions for Practice 8 5 IV. Recommendations and Suggestions for Research 8 6 V. Concluding Comments 8 7 Bibliograpy 8 9 Appendix A 96 Appendix B 99 Appendix C 108 Appendix D 110 Figure D.l 110 Figure D.2 I l l Figure D.3 112 Appendix E 113 Figure E.l 113 Figure E.2 114 Figure E.3 115 Appendix F 116 Figure F.l 116 Figure F.2 117 Figure F.3 118 vi LIST OF TABLES Table 1 Male and Female Students' Career Choice 3 6 Table 2 Average Number of Science Courses Taken in Grade 12 By Male and Female Students 3 8 Tab le 3 Students' Average Number of Grade 12 Science Course Selected by Their Career Choice 3 9 Tab le 4 Students' Grade 11 Science Course Grade Point Average By their Career Choice 4 0 Table 5 Male and Female Students' Grade 12 Science Course Grade Point Average 4 1 Table 6 Career Choice of Female Students by Fathers' Occupation 4 3 Table 7 Career Choice of Male Students by Fathers' Occupation 4 4 Table 8 Career Choice by Female Students' Mothers' Occupation 4 5 Table 9 Career Choice by Male Students' Mothers' Occupation 4 6 Table 1 0 Female Students' Career Choice by Reasons for Grade 12 Course Selection 4 7 Table 1 1 Male Students' Career Choice by Reasons for Grade 12 Course Selection 4 8 Table 12 Variables With Loadings of .4 or More on Factor 1. . . 5 2 Table 1 3 Mean Score of Students' Attitudes Toward Student-Centered Science Activity 5 3 Table 1 4 Variables With Loadings of .4 or More on Factor 2. . . 5 4 Table 1 5 Mean Score of Students' Attitudes Toward "Teacher-Centered Science Activity" 5 4 Tab le 16 Variables With Loadings of .4 or More on Factor 3. . . 55 Tab le 1 7 Mean Score of Students' Attitudes Toward "Science Course Concerns" 5 6 Tab le 1 8 Variables With Loadings of .4 or More on Factor 4. . . 5 6 Table 1 9 Mean Score of Students Attitudes On "Out~OF-School Influence" 5 7 Table 2 0 Variables With Loadings of .4 or More on Factor 5. . . 5 8 Table 2 1 Mean Score of Students' Attitudes Toward "Difficulty" \ 5 8 Table 2 2 Variables With Loadings of .4 or More on Factor 1 (Job) 6 1 Tab le 2 3 Students Mean Score on "Job" Factor 6 1 vu Table 2 4 Variables With Loadings of .4 or More on Factor (Family) 2 62 Table 25 Students' Mean Score on "Family" Factor 63 Tab le 26 Variables With Loadings of .4 or More on Factor (School Success) 3 64 Tab le 2 7 Students' Mean Score on "School Success" Factor. 65 Tab le 28 Variables With Loadings of .4 or More on Factor (School Science) 4 65 Table 2 9 Students' Mean Score on "School Science" Factor. 66 Tab le 3 0 Variables With Loadings of .4 or More on Factor (Science Quality) 5 67 Table 3 1 Students' Mean Score on "Science Quality" Factor. 68 Table 32 Variables With Loadings of .4 or More on FActor (Out-of-School Science) 6 68 Table 33 Students' Mean Score on "Outside Influence" Factor. 69 Table 3 4 Variables With Loadings of .4 or More on Factor (Confidence) 1 7 1 Table 35 Students' Mean Score on "Confidence" Factor. . . 7 2 Table 36 Variables With Loadings of .4 or More on "Social Style" Factor 72 Table 37 Students' Mean Score on "Social Style" Factor . . . 73 Table 3 8 Variables With Loadings of .4 or More on Factor (Working Style) 3 74 Table 3 9 Students' Mean Score on "Working Style" Factor. . 74 Table 4 0 Variables With Loadings of .4 or More on Factor (Thinking Style) 5 75 Table 41 Students' Mean Score on "Thinking Style" Factor. 76 v m ACKNOWLEDGEMENT I would like to thank a number of people who contributed significantly to the production of this thesis. I am grateful particularly to Dr. Jim Gaskell, my thesis advisor, and Dr. Gaalen Erickson who guided my every step of the way from pilot test, data collecting, to data analysis and thesis writing. Their suggestions and advice are not only useful for the completion of my thesis but also for my future research work. In addition, I would like to thank Dr. David Bateson who, having just recovered from illness, took his time to read the thesis and gave me great advice that significantly contributed to the final draft. And also, I would like to thank other friends in the Faculty of Education, especially Dr. Marshall Arlin, Mr. Binzhen Liu, and Dr. Tony Clarke who gave me a lot of wonderful suggestions. Finally, I would like to thank my family in P. R. China who encouraged me a lot while I was studying and working on the thesis in the University of British Columbia, Canada. 1 CHAPTER I INTRODUCTION BACKGROUND The importance of science and technology to the economy in our increasingly technological society has received recent attention by both governmental and non-governmental groups. Reports by the Natural Sciences and Engineering Research Council of Canada (1993) indicate that Canada needs to produce more highly qualified scientists and engineers in the near future to strengthen Canada's economic base: The transformation of global markets is driving all industrialized nations towards knowledge-based economies. These in turn put paramount importance on the intellectual creativity of highly qualified personnel. The demand for people with a background in research is likely to become pressing in the near future. Canada's situation is not enviable. As the Canadian economy moves away from being heavily resource-dependent, the country is confronting a potential shortage of research scientists and engineers necessary to innovate and adapt new technologies. Further more, over the next ten years, both Canada's universities and governments will require doctoral graduates to fill an increasing number of vacancies as the large number of researchers hired in the fifties and sixties retire. To complicate matters, the baby boom generation is moving on and leaving behind a reduction in the university aged population. The number of people in the 18-24 age group is projected to drop 2 drastically by about 1996. The problem is compounded by the decreasing proportional enrollments in some natural sciences and engineering fields. Expanded science and engineering career options and healthy university research environments are critical if we are to be successful at encouraging more people to undertake science and engineering as careers (p.17). It is well documented that in Canada women are under-represented in the science and engineering fields. According to Human Resources Development Canada (1993): The proportion of degrees awarded to women has increased steadily. Women are now granted more than half the degrees at the undergraduate level and close to half at the master's level. Women have also increased their representation at the Ph.D. level, especially in the fields of education, fine and applied arts and the humanities. However, in the fields of engineering and applied sciences and mathematics and physical sciences women form only small minorities of degree recipients (p.18). Women are underrepresented in science and engineering fields in other countries as well, as has been shown by studies conducted in countries such as the United States (Jones & Whelatley, 1988) and the United Kingdom (Kelly, 1981; Fuller, 1991). Many people argue that one way to increase participation in science, is to focus on under-represented groups in science and engineering (e.g. women) and to find ways to increase the presence of these groups in science and engineering fields. To increase participation of underrepresented groups in science, we need to know the answer to the question: "What factors influence whether a student decides to 3 study or choose a career in science or engineering?" In particular, it would be useful to know which factors affect groups, such as male and female students, differently as they consider careers in science and engineering. As a result, a number of researchers and educators have attempted to identify factors that influence female students' academic and career decision-making in the science and engineering areas (Dick & Rallis, 1991; Haggerty, 1991; Jones & Wheatley, 1988; Maple & Stage, 1991; Seymour, 1992; Taber, 1992; Ware & Lee, 1988). An important study exploring these issues has been conducted by Woolnough (1991). His survey study used a questionnaire based on interviews with school heads and teachers and on a review of the literature. Woolnough developed a' simple model of factors that may influence a student's career choice with respect to science and engineering as he/she progresses through school. Students arrive at secondary school with a certain potential, influenced by their ability, their personality and their history. As they progress through school they are influenced by various in-school factors: the type of school and its ethos, the curriculum they are taught, the type of activities they experience (both curricular and extra-curricular) and the career advice they receive. The school's resources, time, money and facilities allocated to science teaching, and the quantity and quality of the science teachers are also considered to be significant factors. There will also be external factors acting, some of which are co-ordinated in the school (for example, links with local industry and work experience). The students' home backgrounds are also 4 influential in the students' academic and career choices. It was hoped that an analysis of such factors in relation to students' career choice would identify significant factors and provide fresh insights into the factors that go into the making of engineers and scientists. The initial work by Woolnough (1991) has been expanded into an international study: Canada along with five other countries (England, Japan, Australia, China, and Newzeland) are all doing similar studies. The present study, as part of this international survey, is seeking to investigate the factors that affect Canadian high school students' future academic and career choices in science and engineering. FOCUS OF THE STUDY The focus of the study is on finding out the factors that influence the academic and career decisions of Grade 12 students in British Columbia. According to Herr (1970), at 17 years of age students are capable of making realistic career decisions. Since Grade 12 students graduating in July, 1994 are at the stage of making decisions as to whether to go to post-secondary education or directly to the job market, most of them have already gone through a long process of weighing the significance of various factors. Therefore, a study of this group of students can provide us with some information about factors influencing their academic and career decisions. This study attempts to examine the differences that exist between male and female students, and between science career choosers and non-science career choosers in terms of the factors that affect their 5 academic and career decision making. Four types of factors are considered: 1) science background; 2) attitudes towards school science; 3) perceptions of various influences on their decision making; 4) perceptions of their own personality. SPECIFIC RESEARCH QUESTIONS 1. How do male and female students differ when they consider their future academic and career choices? 2a. How do science career choosers and non-science career choosers differ in terms of the following elements of "science background": (a) mathematics and science courses they have taken; (b) mathematics and science achievement; (c) their parents' level of education and occupation? 2b. How do male and female students differ in terms of the above elements of "science background"? 3a. How do science career choosers and non-science career choosers differ in terms of their attitudes toward school science activities? 3b. How do male and female students differ in terms of their attitudes toward school science activities? 4a. How do science career choosers and non-science career choosers differ in terms of their perceptions of various encouraging and discouraging factors that affect their career choice? 4b. How do male and female students differ in terms of their perceptions of various factors that affect their career choice? 5a. How do science career choosers and non-science career choosers differ in terms of the perceptions of their own personalities? 6 5b. How do male and female students differ in terms of the perceptions of their own personalities? SIGNIFICANCE OF THE STUDY It is hoped that the results of the study wil l help us to better understand some of the factors that may influence students' academic and career choices, especially the different factors that influence male and female students, and science career choosers and non-science career choosers when they make academic and career choices. This understanding wi l l be useful in helping educators and researchers to find ways to increase the participation of students, particularly of female students, in science courses and careers. Since such a study does not appear to have ever been conducted in Canada, participation in this international study wi l l help us to know what factors influence Canadian students' academic and career choice in science and technology, and also to make possibly make comparisons with studies done in other countries. LIMITATION OF THE STUDY In the sampling procedure, each school was asked to select 20 Grade 12 students randomly from all the Grade 12 students in that school. However, it might be a possibility that some schools just chose an intact class instead of randomly choosing individual students in order to save time. In addition, the ratio of male students to female students in the sample is 1.3 to 1 while the ratio of male to female students of the overall school population in B .C . is 0.96 to 1, this 7 indicates that the sample in this study is to some degree unrepresentative of the population, thus the generalization to the population should be carefully made. 8 CHAPTER II LITERATURE REVIEW INTRODUCTION This chapter reviews the available literature related to the research questions (see Chapter 1). The literature is reviewed according to the following general themes: 1) school science background (course participation); 2) students' attitudes toward school science; 3) perceptions of other factors that influence students' career choice; and 4) students' perceptions of their own personality. SCHOOL SCIENCE BACKGROUND (COURSE PARTICIPATIONS Several authors have concluded that early commitment to, and preparation for, careers in quantitatively based fields (e.g. engineering, physics) is crucial (Berryman, 1983; DeBoer, 1984; Ethington & Woffle, 1988; Hilton & Lee, 1988; Ware, Steckler, & Leserman, 1988). Berryman (1983) pointed out that the quantitative talent pool has emerged by Grade 9 and is essentially complete by Grade 12. After Grade 12, migration is almost entirely out of, not into, the quantitative pool. Thus, the pool from which undergraduate and graduate quantitative degree recipients emerge is essentially formed by the senior year of high school. Hilton and Lee (1988) reported that quantitative fields showed net losses in students at virtually every educational transition point, with the greatest loss 9 occurring in the transition from high school to college, reflecting both a loss to science and loss to higher education. DeBoer (1984), after examining the high school and collegiate science and mathematics participation of a group of students who graduated from a single college, concluded that participation and achievement in high school science and mathematics seems to affect participation and achievement in these fields in college. Therefore, he stressed that early efforts to engage students in science and mathematics coursework are important. Many researches indicate that girls often take fewer senior high school science courses, especially physics, than boys (DeBoer, 1984; Johnson & Bell, 1987; Kahle, Lakes & Cho, 1985; Kelly, 1988; Laurie & Michael, 1985). In the 1990 British Columbia Mathematics Assessment, Gaskell, Arlene, Antoinette, & Linda (1993) studied the patterns of participation of girls and boys in senior level mathematics and science courses in British Columbia. They reported that the gender difference in the course enrollments increases from Grade 11 to Grade 12. At the Grade 12 level the most significant differences are the under-representation of girls in Physics 12 and the under-representation of boys in Biology 12. They compared this with data from the 1989/90 school year. The comparison showed a general trend that a more equal ratio of girls to boys is slowly developing in mathematics and the physical sciences at Grade 12. Since the number of mathematics and science courses taken in high school is a significant variable in women's choice of fields of study 10 (Ethington & Wolfle, 1988), and participation in high school science is obviously a prerequisite for a career in science, by avoiding this subject many female students not only limit their educational experience but also close the door to many future occupational possibilities (Johnson & Bel l , 1987). It is, therefore, important to examine the level of participation of female students in school science courses, and the factors that affect their course choices (Akpan, 1986; Johnson & Bell , 1987; Kelly, 1988; Reyes & Padilla, 1985; Ventura, 1992). The following are a number of factors which the literature indicates influence girls' participation in science courses: science achievement, interest in the subject, perceived ability, prerequisites for entrance to college or university and school teacher. SCIENCE ACHIEVEMENT According to Kelly (1988), in physics and chemistry, achievement in end-of-year examinations has a strong relationship to future course choice. Students who have good performance in science are more likely to choose a science program and pursue a science career than those who do not. Therefore, some researchers believe that poor performance of girls in science courses may be one of the determining factors that affect their choice of participation in senior high school science courses as well as their future fields of study. In science education, there is concern that girls are not achieving as well as boys (DeBoer, 1984; Erickson & Erickson, 1984; Kelly, 1978; Kel ly , 1988; Welch, 1985). Kelly (1978) analyzed an international 11 science dataset for 14-year old pupils in developed countries and found that boys achieved better than girls. The sex differences were consistently large in physics and small in biology. Erickson and Erickson (1984) found from a sample of pupils in grades four, eight and twelve from schools in British Columbia, that males achieved at a higher level in science than did females. The differences were most pronounced in physical sciences, but were also evident in biological sciences. However, average Grade 12 final marks obtained from the British Columbia Ministry of Education reveal little difference between the final marks of male and female grade 12 students in science (Gaskell et al., 1993). Moreover, according to DeBoer (1984), he found that women took fewer science and mathematics courses than men but performed at a higher level both in high school and college. If female students achieve at the same level or above as boys do in science, there may be some other important reasons for their avoidance of mathematics and science courses. INTEREST IN THE SUBJECT Several researchers have conducted studies in order to understand the reasons for students' course choice, and whether there are any differences between boys and girls (Johnson & Bel l , 1987; Taber, 1991; Thomas, 1986; Ventura, 1992). In Johnson and Bell's (1987) research, interest in a subject was one of the most frequently selected reasons for the pupils' subject 12 choices. For example, more girls than boys considered human biology and rural science to be interesting at the time they made their option choices. In contrast, proportionally more boys than girls chose to study electronics and technology for this reason. In Taber's (1992) study, pupils in their first weeks of secondary school were asked to select from a list of those topics they would be interested in studying in science. He found that boys predominantly selected topics with a mechanical connection, while girls predominantly selected topics related to human biology. Ventura (1992) examined the influences on students' subject choice and found that besides ability in the subject, a number of other factors may influence the choice, foremost among which is the pupils' attitude towards the subject. His study indicated that boys are significantly more interested in science than girls. PERCEIVED ABILITY A generally accepted finding is that a student's belief in her/his ability to succeed is a major predictor of later participation (Baker, 1987; DeBoer, 1984; DeBoer, 1987; George, Wystrach & Perkins, 1987). DeBoer (1987) indicated that students who have confidence in their ability in chemistry expected to do well in the future and were more likely to take more chemistry. This is also supported by Fuller (1991) who noted that young people competent at, but not brilliant in, the sciences were discouraged from pursuing science and 13 technology in higher education because they believed science and technology to be peculiarly difficult. In Ventura's study (1992), a list of reasons was provided for pursuing and not pursuing science careers and pupils were invited to mark any number of items. The results showed that the major sex difference was the choice of "I am good at these subjects." This was much more often chosen by boys than by girls, perhaps indicating a lower self-esteem with regard to science on the part of the girls. PREREQUISITES Some students take senior mathematics and physical science courses in order to fulfill university requirements, to keep their options open or to pursue a particular career goal. The Ministry of Education in B .C . , for example, sets requirements for graduation, and universities and colleges set requirements for entrance to their institutions. Entrance to the Faculty of Science at most British Columbia universities, requires the completion of Grade 11 Chemistry and Physics and a Grade 12 science. The heavy concentration of girls in biology rather than physics and chemistry limits their options to enter science at the university level. Similar results have been reported in the United States and England (DeBoer, 1984; Keys & Ormerod, 1976). Why do girls usually see senior mathematics and physics courses as irrelevant to their future? Johnson and Bell (1987) indicated in their study that on the criterion 'usefulness for jobs' the greatest 14 discrepancies between boys and girls emerged for electronics and technology, followed by physics and rural science. In each case significantly greater proportions of boys than girls claimed to have chosen the subject for its occupational value. The science subjects chosen for this reason by slightly higher proportions of girls than boys were biology and human biology. This must be an expected dichotomy in the 'job-value' perceptions of boys and girls, given that their job expectations, even aspirations in many cases, still mirror closely traditional gender-related patterns of employment. The evidence available indicated that physics and even biology are considered by pupils to be important, sometimes essential, for more 'male-appropriate' than 'female-appropriate' jobs. TEACHER It is often said that good science teachers will inspire students to take up careers in these subjects. According to Woolnough (1991), good science teachers had a positive effect at persuading students to continue with the sciences, bad teachers caused students to drop the subjects as soon as possible. Teachers may attract students to a subject or repel them because initially many students assume that mathematics and science are unexciting or hard subjects. Teachers are important in a variety of ways. They interpret the curriculum and they interact with students on a daily basis. They influence the ways students conceive of subjects (their significance, knowledge and activities) and, because of 15 their evaluations of performance, teachers influence how students think about their own abilities (Gaskell et al., 1993). STUDENTS ATTITUDES TOWARD SCHOOL SCIENCE In order to know what schools can do to encourage more students into the physical sciences and engineering, it is important to know what are students' attitudes toward school science. Students' attitudes toward science and science activity in schools have been studied by a number of researchers, for example, Hofman (1977), Mitias (1970), Milson (1972), Gardner (1975), Ormerod & Duckworth (1975), Nash, Allsop and Woolnough (1984). Researchers agree that positive attitudes are essential if students are to obtain the maximum benefit from their work in science. Students' experiences in the classroom and the laboratory should provide challenge, enjoyment and satisfaction. Milson (1972) indicated that a science course should be perceived as a pleasant experience and consequently more positive attitudes should develop toward it. It was found that science students had more favorable attitudes to science than their arts' counterparts (Akpan, 1986; Ormerod & Duckworth, 1975). Woolnough (1991) found that those who were continuing with their physical sciences or engineering found the opportunity to plan their own experiments more satisfying than did those who had given up their sciences, or were about to. Overall it was found that the continuing science students preferred their science activities to be more pupil centered, giving them more opportunity to plan their own 16 experiments and activities and to become involved in science competitions and extended projects, whereas the non-continuing science students preferred their science to be more structured and teacher centered. Past studies have also indicated that students who do not continue in science perceive physical sciences to be difficult. (Akpan, 1986; Ebbut, 1981; Ormerod & Duckworth, 1975; Shannon, Sleet, & Stern 1982). In Akpan's (1986) study, he concluded that students are not willing to study the physical sciences if they regard them as difficult or if science lessons are not enjoyable. This claim is supported by Woolnough's (1991) study: those who were to continue with the sciences found science lessons positive and stimulating, those who rejected the sciences found science lessons negative, difficult and dull. It is clear that if students' attitudes toward science are negative, it is unlikely that these students wi l l benefit from the study of science that is necessary for their survival: they wi l l most likely not become scientists (Ato & Wilkinson, 1983). Boys compared to girls, who continued with science, participated in more classroom science activities, performed more masculine than feminine tasks and had more extracurricular science experiences, according to Kahle and Lakes (1983). Further they noted that boys expressed greater interest in science-related careers than did girls, especially those boys who had more science experiences than their female counterparts. Further, socialization of females toward "doing what the teacher requires" is more accepted for females, whereas 17 males are more often permitted to ignore or reject teacher expectations. According to the research done by Tobin and Garnett (1987), there are gender-related differences in patterns of student engagement in high school science classes. The observational data indicated that males participated to a greater extent than did females in the public interactions with the teacher and in the laboratory activities. Of course, school science experiences are not the only factors that influence students' decisions to choose science or engineering careers. However, they might be one of the most important factors that encourage or discourage students' (male and female) academic and career decision making. PERCEPTIONS OF THE FACTORS THAT INFLUENCE STUDENTS CAREER CHOICE Reasons for students' academic and career choice in science and technology have been studied by several researchers (Dick & Rallis, 1991; Gillian, 1993; Woolnough, 1994). According to Woolnough (1994), there are a number of factors that influence students' career choice into science and engineering. Despite the students' high school background: mathematics and science performance, Grade 11 & 12 course selection and school science experience that might influence their choice of career, other factors also significantly influence students' career choice: job characteristics, family influence and 18 family responsibility. Aspects of these factors will be discussed in the following, sector. JOB CHARACTERISTICS When high school students rated the importance of several values on their choice of an occupation in Tittle's (1981) study, both males and females agreed on the importance of high income, job security and leisure opportunity as components of an ideal job. The research on engineering students done by Lebold (1983) revealed that work-related factors tended to be the most important factors influencing engineering students' career decisions. This is also supported by Gardner and Broadus (1990), for example, for those students pursuing an engineering degree, external reasons like salary, job prospects, job security, promotions and prestige were given as the predominant reasons for pursuing the degree, and within this set of factors, job prospects and salary had the highest ratings. Since occupational status is crucial in determining students' occupational preferences, we can understand, to some extent, why few students choose careers in science-related careers in certain countries such as the United Kingdom. In England, the underlying culture has always been more sympathetic to the intellectuals, and the thinkers, than to the technologists, and the 'doers', and this is reflected in the low status that careers in science and engineering 19 have in society (Galloway, 1991; Wiener, 1981). Surveys have shown that students reflect this, perceiving the jobs of scientists and engineers as having low status, as well as low salaries, compared with such jobs as doctors, solicitors and accountants (Woolnough, 1990) . F A M I L Y INFLUENCE Many researchers found that, with respect to career choice, parents play an important role in a student's decision to continue with sciences (Fulliove, 1987; Franklin & Wong, 1987; Wells & Gaus, 1991) . In Wells and Gaus' research, it was reported that a majority of Kentucky middle school students would be most likely to approach a parent or family member for information about a career. In the studies of Franklin and Wong (1987), they found that 82% of fathers and 89% of mothers reported that their sons and daughters discussed their career choice options with them. In the studies of female engineering, mathematics and science students, parents were most frequently mentioned as the most important influence on young women's decisions to go into science and engineering careers (Campbell, 1992). Similar results can be found in the studies of Lavine (1982), Nash and Woolnough (1984) and Frankling and Wong (1987). In terms of parental influence, parent role models play a crucial part in career development (Lee, 1984; McNair & Brown, 1983). Parents' occupation and level of education have also been investigated as 20 factors influencing students' career decision making (Manis, 1989; Myeong & Crawley, 1991; Nevitte et al., 1988; Handel, 1991; Wells & Gaus, 1991). The most striking fact about parental occupation in the survey responses of University of Michigan seniors is that students in science—particularly women—were more likely to have fathers and mothers in scientific occupations than were other students; of the students in science, 21% of fathers were engineers while 11% of non-science students had fathers who were engineers. Students' home environment: the atmosphere, habits, hobbies and values acquired by students growing up with their parents have particular relevance to science and engineering where, for example, the sharing of mechanical and construction hobbies are influential in shaping a student's interest in science and engineering (Kelly, 1981). F A M I L Y RESPONSIBILITY Since women are underrepresented in careers in science and technology, much research has been done to investigate and explain the low participation rate of women in science and technology (Fuller, 1991; Galloway, 1991; Handel, R. D., 1991; Horst, 1981; Jones & Wheatley, 1988; Maple & Stage, 1991; Rossi, 1965; Ware & Steckler, 1985). The result of Rossi's (1965) and Horst's (1981) research indicated that women did not differ significantly from men in ability or career motivation. However, women were subject to many interpersonal and social barriers to their choice of careers, eg., social isolation, family and career commitments, negative critical incidents and sex discrimination. 21 Fuller (1991) noted that female students in several cases had already considered the issue of combining a science and technology career with having a family. Their perceptions were that science and technology employers, particularly those from the private sector, have reacted more slowly to calls for career breaks and childcare support for working women than have some other professions such as banking. In some instances, this concern has contributed to women's decisions to opt for non-science and technology degrees and career plans. STUDENTS' PERCEPTIONS OF THEIR OWN PERSONALITY When making career choices, the individual tries to co-ordinate self-image and the image of the typical occupational incumbent (Super, 1953). Researchers who have investigated the common image of the scientist and engineer held by the students found out that students have stereotyped images of the scientist (Beardslee & O'Dowd, 1961; Chambers, 1983; Collins & Smithers, 1983; Kelly, 1981; Mead & Metraux, 1957). From these studies, the positive image of a scientist is of a man who is dedicated, careful, involved and highly intelligent, and whose labors will be eventually rewarded by personal success and benefit to mankind. The negative image is of a man engaged in boring, repetitive and unrewarding work, and out of touch with people and human relationships. Careers in science and engineering are perceived by students as matching certain types of personalities. Stereotypically, scientists, especially physical scientists, when compared with non-scientists, tend to be male, to have a high 22 measured intelligence in the non-verbal (spatial) sphere, to be convergent in their thinking and to be emotionally stable, tough-minded, self-sufficient and interested in things rather than people. The image of 'scientist' that emerges from these studies is frequently not very attractive as a career role model and this image may be a deterrent to both sexes. However, it seems that the world of science is clearly a male world, with the perception that science is a masculine activity. The study done by Sjoberg (1988), who drew on Norwegian data, reveals that the image of the typical physicist is at odds with values of importance for girls, and argues that this may be an important factor in explaining why so few girls choose technologically oriented careers. Pupils tend to choose futures where they think people have personalities similar to themselves. In Kelly's "The Missing Half" (1981), one reason she proposes for girls' avoidance of science is that girls see science as masculine, which conflicts with their developing sense of femininity. They also see science as impersonal, whereas their socialization has primarily been towards concern for people. SUMMARY The literature reveals that there are a variety of influences acting on students when they consider future academic and career choices, some arising within schools, some arising in society at large: the ability and aptitudes of the students; their home background; the type of teaching students received in school, whether through the formal science curriculum or through extra-curricula science 23 activities; the perceived characteristics of careers in science and technology and the perceptions of their own personality. In order to know the reasons why some students choose science and technology related careers while some other students choose other areas, it is important to know the difference between science career choosers and non-science career choosers on these important factors. These differences can indicate which factors encourage students continuing with science and which factors discourage them. In addition, in order to promote the participation of female students in science 1 courses and careers, it is important to compare female students with male students on these influences. Such comparisons might help us identify reasons why few female students opt for careers in science and technology related areas. 24 CHAPTER III METHODOLOGY OF THE STUDY INTRODUCnON This chapter explains how the study was conducted. It contains information on who the subjects were, the data collection procedures followed and how the data were analyzed. THE SAMPLE It was planned that a three-way multi-stage cluster sampling strategy would result in a target of four hundred Grade 12 students in British Columbia being asked to participate: 1) Selecting school districts: sixteen school districts in six zones (five from the Lower Mainland, three from the Fraser Valley, three from Vancouver Island, two from the Kooteny, two from the Okanagan, and one from the North) were selected as representative of the B . C . school population. The six zones were used because they make up the traditional sampling framework used by the Ministry of Education and the number of districts from each zone reflects the proportions of students from the zone in the total British Columbia student population. Stratified sampling procedure was applied to ensure appropriate proportional representation of each zone; 2) Selecting schools: twenty schools were randomly selected from these school districts (nine schools from the school districts in 2 5 the Lower Mainland, and one school for each other school district. This was to ensure that the sample would represent proportionally of the population; 3) Selecting students: 20 students in each school (25 copies of questionnaires were sent to each school) were randomly selected by the counselor who identified the students randomly through the procedure described in the letter to the school principal (See Appendix A ) . RETURN OF COMPLETED QUESTIONNAIRES Fifteen schools returned the completed questionnaires (since 25 copies of questionnares were sent to each school, we got 316 completed questionnaires back from the fifteen schools) yielding a return rate of 75%. The five non-responding schools stated that they could not afford the time to participate. INSTRUMENTATION The survey questionnaire (See Appendix B) was similar to the English survey from Oxford University: "The Making of Engineers and Scientists" (Woolnough, 1991). The survey was designed to gather several types of information on the variables to be investigated. Section A : "Student Information Form" asked the students about their background, science achievements and future career choice: subjects being studied in Grade 12; subjects taken in Grade 11; the marks or grades obtained of these subjects; their gender; their parents' education and occupation; career choice; choice for a higher-26 education major; when the career choice was made; and their reason for their Grade 11 and Grade 1 2 course selections. Section B: "Activity in School Science" sought to find out what type of activities in school science lessons were preferred by students. In particular, it anticipated two types of science activity: one, a student-centered approach, in which the students had a significant amount of autonomy for their own learning; the other, a more teacher-centered approach in which lessons were highly structured by the teacher. It also collected data on the perceived value of extra-curricular activities in science such as involvement in science clubs or competitions. This section also sought to measure students' attitudes toward school science in general. In Section B , 2 2 statements were listed about types of activities students might have experienced in their school science courses, and students were asked to mark on a five-point Likert scale whether they strongly agreed, agreed, were neutral, disagreed or strongly disagreed with each statement. Section C : "Factors Influencing Career Choice" sought to measure the strength of factors that might encourage or discourage students in the future to select a career in physical science or engineering. It anticipated that there were some encouraging or discouraging factors relating both to school (eg., success in school, the quality of school science) and out-of-school factors (eg., family environment, job characteristics, extra-curricular activities). In Section C , 3 3 factors which might have encouraged or discouraged students from continuing with their science were listed and space was provided for 27 students to indicate any important factors which were not included in the list. Students were asked to respond to each item using a five-point Likert scale anchored by "very positive" and "very negative". Section D: "How Would You Describe Yourself sought to measure students' personality to see whether such measures related to their tendency to become scientists. Nineteen bipolar adjective pairs (eg., "hard-working"--"lazy", "convergent thinker"--"divergent thinker") were arranged using semantic differential methodology and students were asked to locate themselves between the poles on a seven point scale. Section E : "Additional Comments" provided space for students to comment further, in their own words, on how they decided to, or decided not to pursue a career in science or technology. A few modifications were made to the original instrument in order to make the questionnaire suit the Canadian context and gather additional information. These changes are listed below: In Section A, Item 6, students were asked to note their career choice. The students' career choices were categorized under one of three headings to indicate whether they were potential physical scientists or biological scientists or non-scientists. Also, an item was added where students could provide further written comments on their reasons for choosing Grade 1 2 course options. 28 Four statements were added to Section B: 1) Science helps me to understand things in everyday life, 2) Science involves lots of mathematics, 3) Science courses are more difficult than courses in languages or social studies, and 4) Students in science courses are very aggressive and competitive. These statements were added as a result of information and indications derived from the literature review. A n additional category of response was added. Students could choose "NA" (not applicable) if they thought the statement was irrelevant to them. In Section C , 9 statements were added: 1) Your exam results in mathematics, 2) Your practical ability in doing science, 3) Job satisfaction in a career in science and engineering, 4) Father's occupation, 5) Parental encouragement, 6) Mother's occupation; 7) The availability of jobs in science and engineering, 8) The balance between career and family obligation, and 9) Other factors. These statements were again derived from the literature review. Students' mathematics abilities and practical abilities in doing science have been shown to be linked to students' science achievement, one of the factors that encourages students to continue their sciences. Family influence such as parents' occupation and encouragement has also been shown to be one of the important factors encouraging or discouraging students into science. In terms of job characteristics, the availability of jobs, the balance between career and family obligations and job satisfaction have been shown to be important for students when they make career decisions. Section C ends 29 with an open-ended statement which was designed to el ic i t other factors that might have encouraged or discouraged students when making their choices into or out of science or engineering areas. A s in the previous sector, students could choose " N A " i f they thought the statement was irrelevant to t h e m . Section E was open-ended and was added to explore more deeply why students choose careers in science and engineering or in other areas. Results from this section are beyond the scope of this thesis. The instrument was pi lot tested using a sample of convenience of 30 Grade 12 students prior to the f inal administration of the questionnaire. Results of the pi lot indicated that students had no problems understanding the statements in the questionnaire with the exception of some of the bipolar adjectives from Section D (eg. "covergent th inker" - - "d ivergent th inker" , " in t rover ted"- -"extroverted"). However, the wording was not changed so that future comparisons with the other countries who also have administered the questionnaire w i l l be possible. Other minor problems were also not attended to for the same reason. Overal l the pi lot indicated that the problems were not serious enough to affect the accuracy of the resu l t s . 30 UNrVERSITY AND DISTRICT APPROVAL Before the questionnaires were sent to schools, the project was approved by the Behavioral Sciences Screening Committee for Research Involving Human Subjects, and permission was also granted by each school district and school principal. STATISTICAL ANALYSIS 1. Statistics Methodology for Section A . a) A crosstabulation of sex and career choice (career choice represents future field of study and career choice) was obtained. In terms of career choice, students were grouped as "physical-science career choosers" (physical science indicates one of the physical science-related career choice, such as computer science, electronic engineering etc.), "biological-science career choosers" (biological science indicates one of the biological science-related career choice, such as medicine, marine biology etc.) and "non-science career choosers" (non-science indicates non-science and technology related career choice such as music, business etc.). If a student's future field of study and career choice disagree (one in science, the other in non-science), then his or her choice is regarded as missing data. Ch i -square was applied to see if any differences existed between male and female students' career choice. b) A one-way A N O V A was applied to determine whether male and female students, and physical-science career choosers, biological-science career choosers and non-science career choosers differed 31 significantly in terms of their mathematics and science course selection and mathematics and science achievement. Crosstabulations of male and female students' career choice and their parents' occupation, education and students' reasons for course choice were obtained. Chi-Square was applied to test the relationships among students' career choice and their parents' occupation and education, and students' reasons for course selection. Statistics Methodology for Section B , C and D: Three factor analyses were applied in Section B , Section C and Section D. The factor analyses proceeded separately for Section B, Section C and Section D in the following steps. First, the correlation matrix among all variables was computed. Since one of the goals of factor analysis is to obtain "factors" that help explain these correlations, the variables must be related to each other for the factor model to be appropriate. If the correlations between variables are small, it is unlikely that they share common factors. Bartlett's test of sphericity was used to see if factor analysis was appropriate to apply. Kaiser (1974) characterizes measures in the 0.90's as "marvelous", in the 0.80's as "meritorious", in the 0.70's as "middling", in the 0.60's as "mediocre", in the 0.50's as "miserable", and below 0.50 as "unacceptable". 32 The second step was principal component extraction. The goal of this procedure is to determine the number of common factors needed to adequately describe the data by forming linear combinations of the observed variables. The first principal component is the combination that accounts for the largest amount of variance in the sample. The second principle component accounts for the next largest amount of variance which is uncorrelated with the first. Successive components explain progressively smaller portions of the total sample variance, and all are uncorrelated with each other. The method selected for determining the number of factors to be extracted was based on eigenvalues (the total variance explained by each factor) and the percentage of the total variance accounted for by different numbers of factors. The criteria was that only factors that account for variances greater than 1 should be included. Factors with a variance less than 1 are no better than a single variable, since each variable has a variance of 1 on the eigenvalue scale. The third step was rotation which focuses on transforming the factors to achieve a simpler structure and therefore make them more interpretable. The aim is to have each variable load maximumly on only one factor with minimum loadings on all other factors in order to permit the factors to be differentiated from each other. If several factors have high loadings on the same variables, it is difficult to ascertain how the factors differ. For orthogonal rotation, the varimax method (the most 3 3 commonly used method) which was applied in Section B and Section C , attempts to minimize the number of variables that have high loadings on each factor, thus enhancing the interpretability of the factors. The quartimax method was applied in Section D . This procedure emphasizes the simple interpretation of variables, and seemed more appropriate to use in Section D. In the fourth step, scores for each factor were computed for each case. The method used for estimating factor scores was to single out all those data variables that had factor loadings on each factor above a certain cutoff value selected to allow each variable to load on at least one factor, in this case, .40. The raw scores for these data variables were added up to provide a rough estimate of the factor score for each factor in turn for a given individual. For example, in Section B , in terms of Factor 1 (school science), a student's factor score was calculated by adding all the scores the student had on variables which had high loadings with Factor 1 ("standard experiments, written up correctly give me confidence to continue with science"; "science helps me to understand things in everyday life" etc.), and then divide the total score by 6 (because there were 6 variables loading over .40 on Factor 1). It is a rather crude method, but under some circumstances it may be quite adequate, such as for rough exploratory work and where the variables do not differ greatly among themselves in variability (Comrey & Lee, 1992). These scores were used later in the A N O V A analysis. 34 One-way Anova and Scheffe post-hoc tests were performed for Section B , Section C and Section D to see whether the differences of means between male and female, and among physical science, biological science and non-science students on the factors (as a result of the factor analysis) were significant. The results of these analyses were described in the next chapter. 35 CHAPTER IV RESULTS INTRODUCTION This chapter presents the results obtained from the analyses of the responses to questions in the four sections of the questionnaire (See Appendix B). The presentation of the results corresponds to the research questions presented earlier: 1. How do male and female students differ when they consider their future academic and career choices? 2a. How do science career choosers and non-science career choosers differ in terms of the following elements of science background? (a) mathematics and science courses they have taken, (b) mathematics and science achievement, and (c) their parents' level of education and occupation. 2b. How do male and female students differ in terms of the above elements of science background? 3a. How do science career choosers and non-science career choosers differ in terms of their attitudes toward school science activities? 3b. How do male and female students differ in terms of their attitudes toward school science activities? 4a. How do science career choosers and non-science career choosers differ in terms of their perceptions of various encouraging and discouraging factors that affect their career choice? 4b. How do male and female students differ in terms of their perceptions of various factors that affect their career choice? 36 5a. How do science career choosers and non-science career choosers differ in terms of the perceptions of their own personalities? .5b. How do male and female students differ in terms of the perceptions of their own personalities? GENDER AND CAREER CHOICE Students were asked to report their preferred career choice and future field of post-secondary school study in the questionnaire (see Appendix A , p. 100 ). The analysis of their responses is summarized in Table 1 below. Students were divided into three types: physical-science related career choosers, biological-science related career choosers and non-science related career choosers according to their future field of study and career choice. Table 1 shows that non-science related careers were selected the most frequently (53.1%), followed in order by biological science-related careers (24.3%) and physical science-related careers (22.6%). Table 1 Male and Female Students' Career Choice Male (N) Female (N) Overall (NL) Non-Science 67 88 155 42.9% 64.7% 53.1% Physical-Science 54 12 66 35.3% 8.1% 22.6% Biological-Science 35 36 7 1 21.8% 27.2% 24.3% Total Number 156 136 2920) Chi-square=31.08; df=2; *P=0.050. (1) Note: the missing data are due to the gender not reported and career choice and field of study reported differently. 37 Research question one asked how male and female students differ when they consider their career choice. By looking at Table 1, we can see that there were significant differences between male students and female students in terms of their career choice: while more female students than male students indicated that they would choose non-science related careers (64.7% female verses 42.9% male), a greater percentage of male students (35.3% verses 8.1% female) indicated that they would choose physical science-related careers. In summary, it would seem from the sample of students completing this questionnaire that male students and female students differ significantly in terms of their intended career choice. More female students intend to choose non-science related careers and very few girls intend to choose physical-science related careers in comparison to the intentions of male students. COURSE PARTICIPATION AND CAREER CHOICE 1) Male and Female Students In order to address the second research question, students were asked to identify all the Grade 12 courses that they were currently taking. Table 2 below provides the mean of the number of Grade 12 mathematics and science courses in which the students were enrol led. 38 Table 2 Mean Number of Mathematics and Science Courses^) Taken in Grade 12 by Male and Female Students M a l e Female Overal l mean* 2.71 sd 1.51 n 168 F (1, 305) = 11.2; *P=0.001. (1) No te : Grade 12 mathematics and science courses include the following 14 pre-requisite science courses for the entrance to the university science or applied science department: Math 11, Math 12, Physics 11, Physics 12, Chemistry 11, Chemistry 12, Biology 11, Biology 12, Earth Science 11, Geology 12, Computer Science 11, Computer Science 12. (2) Note: data are missing because of the missing data on gender and course taken . A n F-test indicated that male and female students differed significantly in terms of their Grade 12 mathematics and science course selection; male students selected more mathematics and science courses (pre-requisite for entering university science or applied science department) than did female students. 2) Three Types of Students In order to know whether different types of students select courses differently, in Table 3 the mean of the number of Grade 12 mathematics and science courses was broken down into the three career types: non-science, physical science and biological science. A n 2.13 2.45 1.53 1.54 1 38 306( 2 ) 39 F-test revealed that there was at least one significant difference at the 0.05 level. Post-hoc Scheffe Tests indicated that the non-science type students' number of mathematics and science courses taken was significantly lower than that of the other two types of students. However, there was no significant difference in the number of mathematics and science courses taken between physical-science related career choosers and biological-science related career choosers. Table 3 Students' Average Number of Grade 12 Mathematics and Science Courses Selected By Their Career Choice mean n Non-Science Physical Science Non-Science 1.68 161 \ * Physical-Science 3.20 66 * \ Biological-Science 3.37 7 1 * n s Overall 2.42 2980) \ \ F (2, 297) = 56.48; *P=0.000; ns—-not significant. (1) Note: data are missing because of the missing data on course choice and career choice. Not surprisingly, those students who reported that they intended to pursue a science career selected more mathematics and science courses than students selecting a non-science career. STUDENT ACHIEVEMENT AND CAREER CHOICE 40 l)Three Types of Students In order to explore the relationship between students' career choice and their mathematics and science course G P A , students were asked to identify the Grade 11 courses they had completed along with the corresponding mark or grade they received. In Table 4, the mean grade of students' Grade 11 mathematics and science course was broken down into the three intended career types: non-science, physical science and biological science. Table 4 Students' Grade 11 Mathematics and Science Course Grade Point Average^1) by Their Career Choice mean n Non- Physical science Science Non-Science 4.28 142 / * Physical-Science 4.98 61 * / Biological-Science 4.91 71 * n s Overall 4.60 274(2) / / F (2, 273) = 13.67; *P=0.000. (1) Note: A=6, B=5, C+=4, C=3, P=2, F=l, did not take=0. (2) Note: data are missing because of the missing data on career choice and GPA not reported. An F-test revealed that there was at least one significant difference at the 0.05 level in Grade 11 mathematics and science course G P A for the three types of students . Post-hoc Scheffe Tests indicated that 41 the mathematics and science GPA of non-science students was significantly lower than that of physical science and biological science students. Not surprisingly, those students who reported that they intended to pursue a science career have a significantly higher GPA (mathematics and science courses) than students selecting a non-science career. 2) Male and Female Students In order to determine how male and female students differ in terms of the Grade 11 mathematics and science course GPA, in Table 5 below, the average grade of the students' mathematics and science courses is reported for male and female students. An F-test revealed that the difference between the two groups was not significant at the 0.05 level. Table 5 Male And Female Students' Grade 12 Mathematics and Science Course Grade Point Average Male Female Overall mean 4.57 4.61 4.59 sd 1.12 1.04 1.08 n 154 126 280(D F=0.121, P=0.728, n.s. (1) Note: data are missing because of the gender and GPA not reported. 42 The above data revealed that there was no significant difference in G P A for Grade 11 mathematics and science courses between male and female students. CAREER CHOICE AND PARENTS' OCCUPATION 1) Fathers' Occupation a) Three Types of Female Students To examine the relationship between students career choice and their fathers' occupation, students were asked to identify the nature of their fathers' occupation in the questionnaire (see Appendix B , p.99). These data were then classified into three categories: 1) science and technology related occupations; 2) non-science or non-technology related occupations; 3) don't know. In Table 6 below, there were significant differences among the three types of female students in terms of their fathers' occupations. Female students who chose non-science related careers or biological science-related careers have a low percentage of fathers who are working in science and technology related areas (23.3% and 22.2% respectively), while those who chose careers related to physical-science have a much higher percentage of fathers who work in an area related to science and technology (63.6%). 43 Table 6 Career Choice O f Female Students By Fathers' Occupation N o n - Phys ica l Biological Science Science Science science & technology 23.3% 63.6% 22.2% related occupation non-science & technology 72.1% 36.4% 69.4% related occupation don't know 4.6% 0 8.4% Chi-square = 9.47; df=4; *P=0.050. b) Three Types of Male Students Table 7 presents similar data for the male students. This table shows that there were significant differences among three types of male students in terms of their fathers' occupations. Male students who chose non-science and technology related careers or who chose biological-science related careers had a low percentage of fathers who are working in science and technology areas (19.4% and 20.6%), while those who chose physical-science related careers had a markedly higher percentage of fathers who work in science and technology related areas (38.9%) in comparison with the other two types of students (19.4% and 20.6% respectively). 44 Table 7 Career Choice of Male Students by Fathers' Occupation N o n -Science Phys ica l Science Biological Science science & technology 19.4% 38.9% 20.6% related occupation non-science technology 80.6% 53.7% 79.4% related occupation don't know 0 7.4% 0 Chi-square=15.83; df=4; *P=0.003. In comparing Table 6 and Table 7, it would seem that, to both female and male students, physical-science career choosers had a higher percentage of fathers who work in science and technology related areas than the other two types of students. Besides, for female students who chose physical science careers, their fathers' occupation may have more influence on them than did the fathers of male students; 63.6% of the fathers of female physical science choosers work in science and technology areas while the corresponding proportion for male physical science choosers is 38.9%. 2) Mothers' Occupation a) Three Types of Female Students Students were asked to identify the nature of their mothers' occupation in the questionnaire. These data were then classified and analyzed in an identical manner to those for fathers. Table 8 shows 45 that there were significant differences among the three types of students in terms of their mothers' occupation. Female students who chose non-science related careers had a higher percentage of mothers who are not working in science and technology related areas (90.9%) than did female students who chose physical-science or biological-science related careers (72.7% and 75.0% respectively). Table 8 Career Choice by Female Students Mothers' Occupation Non- Physical Biological Science Science Science science & technology related occupation 6.8% 27.3% 25.0% non-science & technology related 90.9% 72.7% 75.0% occupation don't know 2.3% 0 0 Chi-square=10.13; df=4; *P=0.038. b) Three Types of Male Students Table 9 presents similar data for the male students. This table shows that there were no significant differences among the three types of male students in terms of their mothers' occupation. 46 Table 9 Career Choice By Male Students Mothers' Occupation Non-Science Phys ica l Biological Science Science science & technology 6.0% 12.7% 9.1% related occupation non-science & technology 91.0% 87.3% 90.9% related occupation don't know 3.0% 0 0 Chi-square = 4.21; df=4; p=0.378, n.s. In comparing Tables 8 and 9, it seemed that female students' mothers who work in a science and technology related area appeared to have some influence on their daughters' career choice into science and technology area while, in comparison, male students' mothers' science and technology related occupations didn't seem to have any influence on their sons' career choice into a science and technology area. STUDENTS' REASONS FOR COURSE SELECTION 1) Three Types of Female Students In order to know whether the different types of students have different reasons for course selection, students were asked to choose the reasons for their Grade 11 or Grade 12 course selection (see Appendix B , P.2). Table 10 shows that there were significant differences among the three types of female students in terms of 47 their reasons for their Grade 12 course selection. Female students who chose non-science related careers were much more influenced by "their interest or attraction to the subject" (48.9%), while female students who chose physical science-related careers gave their primary reason as the need to get "pre-requisites for entrance to university"(63.6%). For biological-science related career choosers, both reasons: "attracted by the subjects chosen" and "pre-requisite for entrance to university", seem to be important to them (44.4% and 44.4% respectively). Table 10 Female Students' Career Choice by Reasons for Grade 12 Course Selection Non- Physical Biological Science Science Science attracted by subjects chosen 48.9% 27.3% 44.4% put off by subjects not taken 5.7% 0 5.6% pre-requisite for entrance to univ. 23.9% 63.7% 44.4% attracted by the teacher 0 0 0 no strong reasons 21.5% 0 5.6% other reasons 0 9.1% 0 Chi-square=25.86; df=8; *P=0.001. 2) Three Types of Male Students Table 11 presents similar data for the male students. This table shows that there were significant differences among the three types of male students in terms of the reasons for their Grade 48 12 course selection. Ma le students who chose non-science related careers or physical-science related careers most frequently selected the reason "attracted by subjects chosen" (41.3% and 52.7% respectively) whi le male students who chose biological-science related careers most frequently selected the reasons "pre-requisite for entrance to university" (47.1%). Table 11 Male Students' Career Choice by Reasons for Grade 12 Course Selection N o n - P h y s i c a l B io log ica l Sc ience Sc ience sc ience attracted by subjects chosen 41.3% 52.7% 23 .5% put off by subjects not taken 4.8% 0 2.9% pre-requisite for entrance to univ. 25.4% 34.5% 47 .1% attracted by the teacher 3.2% 0 5.9% no strong reasons 22.2% 9.1% 6.8% other reasons 3.2% 3.6% 11.8% Chi-square = 20.86; df=10; *P=0.022. In comparing Table 10 and Table 11, differences between male and female students can be found in the reasons chosen by physica l -science career choosers. More male students (52.7%) selected courses because they were "attracted by subjects chosen" than did female students (27.3%), and more female physical science choosers (63.6%) selected courses because they were "pre-requisite for entrance to 49 university" than did male students (34.5%). As for biological-science choosers, female students were more attracted by the subjects than were male students (44.4% verses 23.5%). The following are the results of the other questions which were not included above (see Appendix C): 1) In terms of Question 7 (Appendix B , p. 100), there were no significant differences among science career choosers and non-science career choosers, or male students and female students on whether they intended to go to post-secondary education or not; a very high percentage (95%) of them wanted to go to post-secondary education. 2) In terms of Question 9 (Appendix B , p. 100), there were no significant difference among science career choosers and non-science career choosers, or male and female students in terms of the time when they made their decision towards or away from a career in science or engineering. STUDENTS' ATTITUDES TOWARD SCHOOL SCIENCE ACTIVrTIES In order to explore what important factors in school affect students' academic and career choices into the science and engineering areas, students were asked to record how they felt toward various statements about science activities in school (see Appendix B , p. 102). Factor analysis was applied to indicate the important factors among various school science variables. Then, in order to answer research question three, One-Way A N O V A and Scheffe tests were applied to 50 determine how male and female students, and the three classification of students differed in terms of the school factors. The results of the analyses are reported below. 1. Correlation Matrix The 22 variables in Section B were intercorrelated using Pearson product-moment correlation coefficient. When the Bartlett Test of Sphericity was applied, the value of the test was large and the associated significance level was small, so it appeared unlikely that the population correlation matrix was an identity. In addition, the K M O (Kaiser-Meyer-Olkin) measure of sampling adequacy equals 0.76 (see Appendix D , Figure 1), indicating that it was not inappropriate to apply factor analysis (Kaiser, 1974). 2. Factor Extraction The correlation matrix in Table 1 of Appendix D was subjected to principal components extraction. Figure 2 (Appendix D) shows that almost 55.7% of the total variance was attributable to the first seven factors. The remaining sixteen factors together accounted for 44.3% of the variance. Thus, a model with seven factors was considered adequate to represent the data (according to the criteria that only factors that account for variances greater than 1 should be included, which means that the eigenvalue is greater than 1). 3. Factor Rotation After the varimax rotation, the rotated factor pattern matrix was sorted so that variables with high loadings on the same factor appear 51 together (Appendix D, Figure 3). Small factor loadings (less than 0.4) were omitted in the matrix. 1) Factor 1 Factor 1 shows positive correlations among S7, S l , S9, S6, S19, S4, S18, S12 and S l l (see Table 12). The content of these items indicated that Factor 1 could be interpreted as measuring students' attitudes toward "student-centered science activity". 52 Table 12 Variables With Loadings of .4 or More on Factor 1 (Student-Centered Science Activity) Variables Factor Loading S7 An extended practical project shows me what science is like and gets me interested in it .70 S l I find the opportunity to plan my own experiments very satisfying .64 S9 Involvement in science and technology competitions is great fun and useful .63 S6 Involvement in science clubs is helpful in the learning of real science .60 S19 Science helps me to understand things in everyday life .56 S4 I value the opportunity when the teacher lets us plan our own activities in science courses .55 S18 Local scientists and engineers can bring a stimulating dimension into science courses .53 S12 Standard experiments, written up correctly, give me confidence to continue with science .52 S l l School science should be about learning to do science through scientific investigations .42 In terms of "student-centered activity", no significant difference was found between male and female students. Among the three types of 53 students, the only significant difference that was found was between non-science career choosers and biological-science choosers, it seems that biological-science choosers had more positive attitudes toward "student-centered activity" than non-science choosers (see Table 13). Table 13 Mean Scored) of Students' Attitudes Toward Student-Centered Science Activity M F Non-Science Physical-Science Biological-Science Student- 3.74 3.68 3.63*c 3.77 3.93 Centered I p<.05 I F(2, 158)=4.51, P<0.05. (1) Note: mean score of each student on Factor 1 =(S7+Sl+S9+S6+S19+S4+S18+S12+Sll)/9. *c—significant difference between Biological Science and Non-Science 2) Factor 2 Factor 2 shows positive correlations among S2, S3 and S5. (See Table 14). From the content of these items, Factor 2 can probably be interpreted as measuring students' attitudes toward "teacher-centered science activity". 54 Table 14 Variables With Loadings of .4 or More on Factor 2 (Teacher-Centered Science Activity) Variables Factor Loading S2 Student work should be marked objectively .69 by the teacher S3 School science should be about learning .58 scientific facts and theories S5 I feel most confident when the science .57 courses are highly structured and teacher directed In terms of "teacher-centered science activity", there were no significant differences among male and female students, and the three types of students (see Table 15). Table 15 Mean Scored) of Students' Attitudes Toward "Teacher-Centered Science Activity" M F Non-Science Physical-Science Biological-Science Teacher- 3.63 3.66 3.58 3.66 3.79 Centered (1) Note: mean score of each student on Factor 2=(S2+S3+S5)/3. 5 5 3) Factor 3 Factor 3 shows positive correlations with S20 and S22 (see Table 16), and can be interpreted as measuring students' attitudes toward "science course concerns". Table 16 Variables With Loadings of .4 or More on Factor 3 (Science Course Concerns) Var iables Factor Loading S20 Science involves lots of mathematics .73 S22 Students in science courses are very .71 aggressive and competitive No significant difference was found between male and female students. However, a significant difference was found within the three types of students in terms of science course concerns (see Table 17). It seems that physical-science career choosers more strongly agreed that "science involves lots of mathematics" and "students in science courses are very aggressive and competitive" than did biological-science choosers. 56 Table 17 Mean Scored) of Students' Attitudes Toward "Science Course Concerns" M F Non-Science Physical-Science Biological-Science Science 3.81 3.75 3.73 3.99 3.66*b Course I p<.05 I Concern F(2,278)=3.54, P<0.05. (1) Note : mean score of each student on Factor 3=(S20+S22)/2. *b significant difference between Physical Science and Biological Sc ience . 4) Factor 4 Factor 4 consisted of S16, S14 and S15. (see Table 18) and therefore might be interpreted as measuring students' attitudes toward "out-of-school influence". Table 18 Variables With Loadings of .4 or More on Factor 4 (Out-of-School Influence) Var iab les Factor L o a d i n g S16 The times when the school suspends its normal time table for extended projects are not very useful .76 S14 Work experience in science based industry turns people off jobs in science or engineering .53 S15 Parents should not be involved in the work of the school science department .47 57 No significant differences were found among the three types of students in terms of outside influence. However, it seems that male and female students differ significantly; male students more strongly agreed with the statements about "out-of-school influence" than did female students (see Table 19). Table 19 Mean Scored) of Students' Attitudes On "Out-of-School Influence" Factor M F Non-Science Physical-Science Biological-Science Out-Of- 2.86 2.52* 2.75 2.72 2.54 School l_P<.05_l Influence F ( l , 186)=6.42, P<0.05. (1) Note: mean score of each student on Factor 4=(S16+S14+S15)/3. * significant difference between male and female students. 5) Factor 5 Factor 5 consisted of only S17 and S21. (see Table 20) and from the content of these items, Factor 5 can be interpreted as measuring students' attitudes toward "difficulty". 58 Table 20 Variables With Loadings of .4 or More on Factor 5 (Difficulty) Variable Factor Loading S17 I find I need to write quite a lot to really .78 express myself clearly S21 Science courses are more difficult than .61 courses in languages or social studies ' In terms of "difficulty", significant differences were found between male and female students, and among the three types of students. Female students more strongly agreed with the "difficulty" of science than did male students and non-science choosers more strongly agreed with the "difficulty" of science than did physical science choosers (see Table 21). Table 21 Mean Scored) of Students' Attitudes Toward "Difficulty" M F Non-Science Physical-Science Biological-Science Difficulty 3.16 3.50* 3.51 2.92*a 3.28 l_p<.05_l 1 p<.05 1 F(l, 297)=9.65, P<.05. F(2, 288)=3.45, P<0.05. (1) Note: mean score of each student on Factor 5 =(S17+S21)/2. *a—significant difference between Non-Science and Physical Science. *—significant difference between male and female students. 59 STUDENTS PERCEPTIONS OF THE FACTORS THAT AFFECT THEIR CHOICE OF CAREER In order to explore the factors that encourage students to consider or discourage students from considering science or engineering careers, students were asked to point out the factors that might influence or had influenced their career choices (See Appendix B , p. 104). Factor analysis was applied to suggest the important clusters (factors) among various factors. Then, in order to answer Research Question Four, One-Way Anova and Scheffe tests were applied to test how male and female students and the three types of students differed in terms of these important factors. The results of the analyses are reported below. 1. Similar factor analysis procedures were used here as in the previous section. The 33 variables in Section C were intercorrelated using Pearson product-moment correlation coefficients. The value of the Bartlett's test of sphericity was large and the associated significance level was small. In addition, the K M O measure of sampling adequacy was 0.82 (see Appendix E , Figure 1), which indicates that using factor analytic techniques was acceptable. 2. Similar factor extractions were applied as were used for Section B . The correlation matrix in Figure 1 was factor analyzed using principal components analysis. Figure 2 (Appendix E) shows that almost 62.1% of the total variance is attributable to the first eight factors. The remaining 25 factors together account for only 37.9% of the variance. Thus, a model with eight factors (eigenvalues greater 60 than 1) appeared adequate to represent the data. However, the following analysis, only six factors were analyzed because the last factor turned out to be related with only one variable, and the seventh factor was not interpretable, so they were all omitted. 3. After the orthogonal rotation, however, as we can see from Figure 3 (Appendix E) , the number of large and small factor loadings increases. Variables are more highly correlated with single factors. Interpretation of the factors also appears possible. 1) Factor 1 Factor 1 has high and medium positive correlations with C25, C27, C28, C26, C29, C8 and C18 (all the loadings are above 0.4). Since C8 and C18 had meaningfully higher correlations with Factor 5 and Factor 6, they were excluded from Factor 1. Thus five items represented Factor 1, (see Table 22). Factor 1 can be interpreted as measuring students' perceptions of "job" factors that might affect their career choice. 61 Table 22 Variables With Loadings of .4 or More on Factor 1 (Job) Var iab les Factor L o a d i n g C25 The social status of jobs in science and engineering .77 C27 Work experience in local companies .75 C28 The availability of jobs in science and engineering .73 C26 The salaries offered in science and engineering .70 jobs C29 Involvement in science clubs .55 In terms of the factor of "job" characteristics that have influenced and might influence students' career choice in one of the physical sciences or engineering, there were no significant differences found between male and female students, and between science related career choosers and non-science related career choosers. Generally speaking, the average score of all the students was above 3.0 (neutral), so Factor 1 can be regarded as an encouraging factor (see Table 23). Table 23 Students' Mean Scored) on "Job" Factor M F Non-Science Physical-Science Biological-Science Job 3.60 3.65 3.46 3.55 3.88 (1) Note: mean score of each student on Factor l=(C25+C27+C28+C26+C29)/5. 62 2) Factor 2 The second factor had high and medium positive correlations with C24, C20, C23, C31, C22 and C21, (see Table 24) and from the content of these items, Factor 2 can be interpreted as measuring students' perception of their "family" which may affect their career choice. Table 24 Variables With Loadings of .4 or More on Factor 2 (Family) Var iab les Factor L o a d i n g C24 Mother's occupation .83 C20 Father's occupation .80 C23 Experiences of your family in science related .72 indus try C31 The balance between career and family obligations .65 C22 Parental encouragement .49 C21 Scientific hobbies or tinkering with gadgets at .43 home In terms of the "family" factor, there were significant differences between male and female students, and between non-science related career choosers and physical science related career choosers. It can be interpreted that male students regarded their family environment as more of an encouraging factor than did female students, and physical science career choosers regarded family environment as a more encouraging factor than did non-science career choosers. 6 3 However, generally speaking, the overall average score of students on this factor was below 3.0 (neutral). This indicated that students regarded their "family" factor as discouraging them toward science and engineering related careers (see Table 25). Table 25 Students Mean Scored) On "Family" Factor M F Non-Science Physical-Science Biological-Science Family 2.69 2.36* 2.41 2.85*a 2.68 Lp<0.05_l I p<0.05 I F(l,295)=6.80, P<0.05. F(2,288)=3.94, P<0.05. (1) Note: mean score of each student on Factor 2=(C24+C20+C23+C31+C22+C21)/6. *a—significant difference between Non-Science and Physical Science. *—significant difference between male and female students. 3) Factor 3 The third factor had high and medium positive correlations with C3, C2, C I , C19, C15 and C4. Since C19 was more meaningfully correlated with Factor 6 than with Factor 3, it was excluded from this factor. Factor 3 can be interpreted as measuring students' perceptions of their "school success" which may affect their career choice (see Table 26). 64 Table 26 Variables With Loadings of .4 or More on Factor 3 (School Success) Var iab les Factor L o a d i n g C3 Your exam results in mathematics .78 C2 Your exam results in science .76 CI Your intellectual satisfaction in doing science .61 C15 The possibility of funding to study science or .47 engineering in college or university C4 Your practical ability in doing science .45 In terms of the "school success" factor, there were significant differences between male and female students, and between non-science career choosers and science career choosers. This can be intepreted that male students thought their "school success" factor was more encouraging to them toward a science related career than did female students and science choosers regarded their "school success" factor as more encouraging than did non-science students. The overall average score indicates that students regarded this factor as encouraging rather than discouraging them into science related careers (see Table 27 below). 65 Table 27 Students Mean Scored) on "School Success" Factor M F Non-Science Physical-Science Biological-Science School 3.53 3.28* 3.08*c 3.72*a 3.83 Success l_p<0.05_l I p<0.05 I ' 1 p<0.05 1 F(l,279)=6.33, P<0.05. F(2,273)=33.49, P<0.05 (1) Note: mean score of each student on Factor 3=(C3+C2+Cl+C15+C4)/5. *a significant difference between Physical-Science and Non-Science *c—s ign i f i can t difference between Biological Science and Non-Science *—sign i f i can t difference between male and female students 4) Factor 4 Factor 4 had high and medium positive correlations with CIO, C.12, C l l and C14, (see Table 28). Thus, Factor 4 can be interpreted as measuring students' perceptions of "school science" which may affect their career choice. Table 28 Variables With Loadings of .4 or More on Factor 4 (School Science) Variables Factor Loading CIO The amount of work required for school science .74 C12 The amount of self-expression allowed in .71 science courses C l l The practical nature of science courses .64 C14 The personal encouragement given by science .49 teachers 66 In terms of the "school science" factor, no significant difference was found between male and female students. However, a significant difference was found between science related career choosers and non-science choosers. Science choosers tended to think that "school science" was more encouraging with respect to a science and engineering career than did non-science students. However, the overall average score of the non-science students was below 3.0 (neutral) which shows that non-science students regarded this factor as discouraging with respect to science, while the average score of science choosers was above 3.0, which indicated that physical science and biological science career choosers regarded this factor as encouraging (see Table 29). Table 29 Students Mean Scored*) On "School Science" Factor M F Non-Science Physical-Science Biological-Science School 3.18 2.97 2.85*c 3.38*a 3.44 Science I p<0.05 I I p<0.05 I F(2,290)=14.94, P<0.05. (1) Note: factor score of each student on Factor 4=(C10+C12+C1 l+C14)/4. * a significant difference between Non-Science and Physical Science. *c—s ign i f i can t difference between Non-Science and Biological Science 5) Factor 5 Factor 5 had high and medium positive correlations with C7, C5, C4, C8 and C6. C4 was more meaningfully correlated to Factor 3, and so 67 it was excluded from this factor. , Thus, Factor 5 can be interpreted as measuring students' perceptions of a "science course quality" which may affect their career choice (see Table 30). Table 30 Variables With Loadings of .4 or More on Factor 5 (Science Course Quality) Var iab les Factor Loading C7 The quality of technology courses .68 C5 The quality of science courses .63 C8 The amount of involvement with human .57 issues in science courses C6 The level of difficulty of the science at school .50 In terms of "science course quality" in school, a significant difference was found only between non-science choosers and biological science choosers. The result shows that biological science choosers thought this factor encouraged them more into a science-related career than did non-science choosers. No significant difference was found between male and female students. The overall average score was above 3.0, indicating that all the students regarded this factor as encouraging them into science related careers (see Table 31). 6 8 Table 31 Students Mean Scored) On "Science course quality" Factor M F Non-Science Physical-Science Biological-Science Science 3.43 3.50 3.31*c 3.56 3.71 course I p<0.05 I quali ty F(2,219)=5.08, P<0.05. (1) Note: factor score of each student on Factor 5=(C7+C5+C8+C6)/4. *c—significant difference between Non-Science and Biological Science 6) Factor 6 Factor 6 had high and medium positive correlations with C17, C18, C16, C32 and C19 (see Table 32). Factor 6 can be interpreted as measuring students' perceptions of "Out-of-School Science" factor which may affect their career choice. Table 32 Variables With Loadings of .4 or More on Factor 6 (Out-of-School Science) Variables Factor Loading C17 Local scientists and engineers coming into school .81 C18 Visits to science firms .65 C16 Involvement in science competitions .56 C32 The situation in local science based industry .53 C19 Job satisfaction in a career in science and engineering .43 69 In terms of the "out-of-School Science" factor, no significant difference was found between male and female students, but a significant difference was found between non-science students and science students. Physical science and biological science career choosers regarded this factor as encouraging them into science related career while non-science students regarded this as a discouraging factor. The overall score was above 3.0, indicating that generally speaking, students regarded this factor as encouraging (see Table 33). Table 33 Students Mean Scored) On "Out-Of-School Science" Factor M F Non-Science Physical-Science Biological-Science Out-of- 3.11 2.95 2.82*c 3.18*a 3.45 School I p<0.05 I Science I p<0.05 I F(2,209)=10.59, P<0.05. (1) Note: factor score of each student on factor 6=(C17+C18+C16+C32+C19)/5. *a significant difference between Non-Science and Physical Science *c—significant difference between Non-Science and Biological Science STUDENTS PERCEPTIONS OF THEIR OWN PERSONALITY In order to find out how students' perceptions of their own personality might relate to their career choice, students were asked to indicate how they would describe themselves (see Appendix A, p. 106). Factor analysis was applied to determine the important 70 clusters (factors) among various personality factors. Then, in order to answer Research Question five, One-Way Anova and Scheffe tests were applied to test how male and female students, and the three types of students differed in terms of these important personality factors. The results of the analyses are reported below: 1. Similar factor analysis procedures were applied here as in Section B and Section C . The 19 variables in Section D were intercorrelated using the Pearson product-moment correlation coefficient. The value of the Bartlett's test of sphericity is large and the associated significance level is small. In addition, the K M O measure of sampling adequacy equals 0.74 (see Appendix F , Figure 1), indicating that it is permissable to use factor analysis. 2. Figure 2 shows that 60.1% of the total variance is attributable to the first six factors. The remaining thirteen factors together account for only 39.9% of the variance. Thus, a model with six factors was determined to be adequate to represent the data (eigenvalues are greater than 1). However, since factors five and six were not interpretable, they were omitted in the following analysis. 1) Factor 1 The first factor had positive correlations with P4, P14, P17, P16, P2 and PI9, and a negative correlation with P3 (see Table 34). Factor 1 can be interpreted as measuring something like "confidence". Although P19 seems to have medium loadings under Factor 2 also, it was included in Factor 1 since it had a higher correlation with 71 Factor 1. Table 34 Variables With Loadings of .4 or More on Factor 1 (Confidence) V a r i a b l e Factor L o a d i n g P 4 Self-confident .76 P 1 4 Dominant .74 P 1 7 Self-sufficient .68 P 1 6 A d v e n t u r o u s .58 P 2 Clever .52 P 1 9 Enthusiastic .46 P3 Introver ted -.45 Individuals who were scored high on this personality factor tended to be more self-confident, dominant, self-sufficient, adventurous, clever, enthusiastic, and extroverted. Individuals who were low on this factor regarded themselves to be more insecure, submissive, timid, stupid, sober and introverted. In terms of the self-confidence, no significant differences were found among three types of students, or between male and female students, which indicates that students had similar degrees of self-confidence (see Table 35). 72 Table 35 Students' Mean Score On Factor 1 (Range=7-1) M F Non-Science Physical-Science Biological-Science Self- 5.23 5.11 5.22 5.23 5.14 Confidence F(l,300)=4.42, P<0.05. (1) Note: factor score of each student on Factor 1 =(P4+P 14+P17+P16+P2+P19+8 -P3 )/7. 2) Factor 2 The second factor had positive correlations with P 9 (interested in people), P 13 (communicating best in words), and P 12 (gregarious) (see Table 36). Thus Factor 2 can be interpreted as measuring "social style". Table 36 Variables With Loadings of .4 or More on Factor 2 (Social Style) Var iab les Factor • L o a d i n g P 9 Interested in people .74 P I 3 Communicating best in words .68 P I 2 Gregarious .55 P 6 V e r b o s e .44 Individuals who were high on this factor tended to be more interested in people, could communicate best in words, and were 73 gregarious and enthusiastic. Individuals who were low on this factor were inclined to be interested in ideas, communicate best in diagrams, be a loner and sober. In terms of the social style, there were significant differences between male and female students, and among all of the three types of students. Female students thought themselves to be more social than male students, Non-science related career choosers thought themselves to be the most social, followed by biological science-related career choosers, and the physical science-related career choosers (see Table 37). Table 37 Students' Mean Scored) On Factor 2 (Range=7-1) M F Non-Science Physical-Science Biological-Science Social 4.29 4.85* 4.92*c 3.84*a 4.45*b Style l_p<0.05_l I p<0.05 I I p<0.05_ I I p<0.05 I F(l,297)=22.97, P<0.05. F(2, 289)=23.44, P<0.05. (1) Note: factor score of each student on Factor 2= (P9+P13+P12+P6)/4. *a—sign i f i can t difference between Non-Science and Physical Science *b—sign i f i can t difference between Physical Science and Biological Science *c—s ign i f i can t difference between Non-Science and Biological Science *—sign i f i can t difference between male and female students. 3) Factor 3 Factor 3 had positive correlations with P15, PI and P5 (see Table 38). Factor 3 can be interpreted as measuring "working style". 74 Table 38 Variables With Loadings of .4 or More on Factor 3 (Working Style) Var iab les Factor Loading P I 5 Conscientious .71 P I H a r d - w o r k i n g .67 P5 Task-centered .58 Individuals who were high on this factor regarded themselves as more conscientious, hardworking, and task-centered, while those who were low on this factor regarded themselves to be more casual, lazy and person-centered. In terms of working style, no significant differences were found among three types of students, or between male and female students. This indicates that all students generally preferred a similar type of working style (see Table 39). Table 39 Students' Mean Scored) On Factor 3 (Range=7-1) M F Non-Science Physical-Science Biological-Science W o r k i n g 4.52 4.70 4.49 4.64 4.86 Style (1) Note: factor score of each student on Factor 3= (P15+Pl+P15)/3. 75 4) Factor 4 Factor 4 had positive correlations with P8 and P10 (see Table 40). Factor 4 can be interpreted as measuring "thinking style". Table 40 Variables With Loadings of .4 or More on Factor 4 (Thinking Style) Var iab les Orthogonal P8 Abstract thinker .82 P 1 0 Creative .76 Individuals who scored high on this factor were more abstract and creative in their thinking while those who scored low on this factor tended to be practical and systematic in their thinking. In terms of thinking style, there was a significant difference found between non-science choosers and biological science choosers. Non-science related career choosers regarded themselves as more creative and abstract in their thinking than did biological science-related career choosers. There was no difference found between male and female students (see Table 41). 76 Table 41 Students Mean Scored) On Factor 4 (Range=7-1) M F Non-Science Physical-Science Biological-Science T h i n k i n g 4.32 4.49 4.67*c 4.35 3.93 Style I p<0.05 I F(2,289)=6.11, P<0.05. (1) Note: factor score of each student on Factor 4=(P8+P10)/2. *c—s ign i f i can t difference between Non-Science and Biological Science. The next chapter contains the discussion of the results, recommendations and researcher's concluding comments. 77 CHAPTER V DISCUSSION AND IMPLICATIONS INTRQDUCnON This chapter is organized into three sections. In the first section, a discussion of the results in the last chapter are provided. This discussion is divided into five subsections relating to the five research questions. In the second section, recommendations are provided regarding related topics needing further research in order to clarify issues addressed by this study. The final section contains the researcher's concluding comments. DISCUSSION GENDER AND CAREER CHOICE It was found in this study that male and female students differ significantly in terms of their career choices. Fewer female students intend to select science related careers, especially physical science related careers, in comparison with male students (8.1% versus 35.3%). This finding confirms the results of previous research done in Canada (Human Resources Development Canada, 1993) and elsewhere (Jones & Whelatley, 1988; Kelly, 1981; Fuller, 1991). It is very clear from this sample that female students preferred non-science careers (64.7%) over physical-science (8.1%) and biological-science related careers (27.2%). Male students, it seems, showed a little more interest in non-science (42.9) and physical-science related careers (35.3%) than in biological-science related careers (21.8%). 78 Generally speaking, the gender difference in career choice lies in the physical-science careers into which fewer female students intend to go. In order to find a way to increase students' participation in science (especially female students), it is therefore important to identify factors that influence male and female students, and non-science choosers and science choosers when they consider their academic and career choices. It is also important to examine how these factors affect students differently. SCIENCE B A C K G R O U N D In terms of Grade 12 course selection, male students selected more mathematics and science courses than did female students; science career choosers who reported that they intended to pursue a physical-science or biological-science related career selected more mathematics and science courses than students selecting a non-science career. This finding was confirmed by the results of previous research (DeBoer, 1984; Kahle et al., 1985; Kelly, 1988; Laurie & Michael, 1985). In addition, male and female students differed significantly in terms of their Grade 12 mathematics and science course selection: male students selected more mathematics and science courses than did female students. According to Johnson and Bel l (1987), by avoiding school science, many female students not only limit their educational experience but also close the door to many future occupational possibilities; thus it is important to know their reasons for avoiding science. One main reason may be differences in science achievement. From this study, it seems that the G P A of non-science career choosers was significantly lower than 79 that of physical-science and biological-science career choosers. However, no significant difference was found between male and female students. This is consistent with Gaskell et al. (1993), who reported that the average Grade 12 final marks obtained from the British Columbia Ministry of Education reveal little difference between the final marks of male and female Grade 12 students in science. If females achieve at the same level or above as males do in science, there may be some other important reasons for their avoidance of mathematics and science courses as well as science related careers, especially physical-science careers. One finding from the study in terms of the reasons for course selection shows that more male physical-science choosers (52.7%) selected courses because they were "attracted by subjects chosen" than did female students (27.3%). More female physical-science career choosers (63.6%) selected courses because they were pre-requisite for entrance to university than did male students. This finding confirms what has been found in previous research; male students are more interested in science than female students. From the study, it also turns out that both male and female physical-science career choosers had a higher percentage of fathers who work in science and technology related areas than did the other two types of students. In addition, for female physical-science choosers, their mothers' occupation appears to have some influence on their choice of a science and technology areas while male students' mothers' 80 occupation did not seem to have any influence on their sons' career choice into science and technology areas. ATTITUDES TOWARD SCHOOL SCIENCE ACTIVITY As to the activities in school that may affect students' choosing a career in science and engineering, overall male and female students, and the three types of students in the survey expressed overall similar attitudes toward "student-centered science activity". One significant difference was found between biological-science choosers and non-science choosers in which biological-science choosers had more positive attitudes toward "student-centered science activity" than did non-science choosers. In terms of "teacher-centered activity", no difference was found between male and female students, or among the three types of students. However, according to previous research, female students and non-science students preferred their science to be more structured and teacher-centered (Woolnough, 1991), a finding which was not confirmed in this study. In terms of the factor called "difficulty", significant differences were found between male and female students, and among the three types of students. Female students more strongly agreed with statements about the "difficulty" of science than did male students. Non-science choosers more strongly agreed with statement about the "difficulty" of science than did physical science choosers. These findings support 81 much previous research (Akpan, 1986; Ebbut, 1981; Ormerod & Duckworth, 1975; Shannon et al., 1982). With regard to science course concerns, physical-science choosers were found to differ significantly from the other two types of students. It seems that physical-science students thought they were more "aggressive and competitive" than did biological-science students, and thought that their "science" involved more mathematics than did biological-science students. Male and female students differed significantly in terms of the factor called "out-of-school influence". Male students agreed more on "oiit-of-school influence" that attract students away from school science; it seems that male students in the survey did not have positive attitudes toward the influences from outside school. However, the finding might be affected by the large number of missing values (126 out of 316). PERCEPTIONS O F T H E F A C T O R S T H A T I N F L U E N C E STUDENTS C A R E E R CHOICE Generally speaking, in the survey, science choosers reacted more positively than did non-science choosers, and male students had more positive attitudes than did female students to the factors encouraging them into science and technology careers. Male students regarded their "family" and "school success" factors as more encouraging than did female students, and science choosers regarded "family", "school success", "school science" and "out-of-school science" 82 as more encouraging factors than did non-science choosers. In addition, biological-science choosers thought "science course quality" in school to be a more encouraging factor than did non-science students. Students similarly regarded the "job" factor as encouraging. This confirmed previous research (Tittle, 1981; Lebold et al., 1983) that students agreed on the importance of high income, job security etc. as components of an ideal job. In terms of the "family" factor, male students regarded their family environment as a more encouraging factor than did female students, and physical science career choosers regarded this factor as more encouraging than did non-science career choosers. It has been previously found that family environment plays an important role in students' decision to continue with sciences (Fulliove, 1987; Franklin and Wong, 1987; Wells & Gaus,1991), and parents were most frequently mentioned as the most important influence on a young women's decisions to go into science and engineering careers (Campbell, 1992). However, the overall average score of students on this factor was below 3.0 (neutral). This indicates that students regarded their "family" factor as discouraging them from science and engineering related careers. That female students' scores were significantly lower than those of male students indicated that their families influenced them more away from science choices than did male students' families. 83 In terms of the "school success" factor, male students thought their "school success" as encouraging them more towards science related careers than did female students. This finding is very interesting because it turns out from this study that males and females had similar science G P A s . Male students, however, seem to be more confident about their abilities in science than are female students and female students seem to have a low self-esteem which may affect their choices of careers in science and engineering. This finding is supported by previous research of Ventura (1992). Since a student' belief in her/his ability to succeed is a major predictor of later participation (Baker, 1987; DeBoer, 1987), it is important to have confidence in one's own ability. Not surprisingly, physical-science choosers in the study thought of their "school success" as encouraging them more towards science related careers than did non-science choosers. In terms of "school science", "out-of-school science" and "science course quality", no differences were found between male and female students. Science choosers, however, had more positive attitudes toward these factors than did non-science choosers. These factors were all regarded by students as encouraging them into science and engineering careers. Therefore, these factors can be seen as important factors that influence students into or away from science and engineering areas. 84 STUDENTS PERCEPTIONS OF THEIR OWN PERSONALITY In terms of students' perception of their own personality, significant differences were found between male and female students, and among the three types of students in terms of "social style". Female students saw themselves as more social than male students. They thought of themselves as more "interested in people", more "communicating best in words" and more "gregarious" than male students. Students have an image of the scientist who is a man engaged in boring, repetitive and unrewarding work, out of touch with people and human relationships. Therefore, female students' avoidance of science could be because they see science as impersonal, whereas their socialization has primarily been towards concern for people (Kelly, 1981). The findings from this study seem to support this. Female students thought themselves more sociable and very few of them selected science-related careers. Among the three types of students, it seems that non-science students saw themselves as more social than science choosers. Biological-science choosers saw themselves as more social than physical-science choosers. This finding seems to be supportive of the previous research: students think scientists, especially physical scientists tend to be male, to have a high measured intelligence in the non-verbal (spatial) sphere, and are interested in things rather than people (Kelly, 1981). It turns out from this study that physical-science career choosers had the lowest scores in social style. 85 RECOMMENDATIONS AND SUGGESTIONS FOR PRACTICE 1) In this survey, female physical-science career choosers tended to select mathematics and science courses because they were "prerequisite for university" rather than because the students were "attracted by the course". Almost no students selected science courses because they were "attracted by the teacher". Therefore in order to encourage more students, especially female students, to choose science and engineering, school teachers should consider measures to make mathematics, science courses and extra-curricular activities a more attractive experience for both male and female students. In addition, teachers should help female students believe that they can do as well as male students. This is important because in the study male students were more confident about their "school success" than female students, and female students thought science more difficult than male students even though there was no difference found between male and female students' Grade 11 mathematics and science GPA. 2 ) Since the "family" factor in the study is a discouraging one, especially to female students, it seems that school teachers should also co-operate with students' parents to encourage students to consider science since parents play an important role in students' academic and career decision. 3 ) The study suggests that students' self-perceived personality may affect their choice of career. Those students who selected non-86 science careers saw themselves as more social than science students. "Scientists" are typically seen as having a high measured intelligence in the non-verbal(spatial) sphere and as being task-centered and interested in things rather than people. It is important to correct this stereotyped image of "scientist", and students should be shown what kind of work scientists do and what kind of persons scientists are. Thus more visits to firms or more scientists coming to school are recommended. RECOMMENDATIONS AND SUGGESTIONS FOR RESEARCH 1) Further research can be done on exploring students' points of view about school science since there are some data missing in the study. Different schools may offer different types of school activity, and different qualities of teaching, and therefore, comparisons can be done among schools to see which schools can successfully encourage students into science and engineering careers. The experiences of successful schools can be good examples for other schools. 2) Some other statistics methodology such as discriminate function analysis could be applied to identify the most important factors affecting students' career choices for male or female students. Path-analysis could be applied to find the relationships among these factors and in the end find out how the factors affect students' career choice. 87 CONCLUDING COMMENTS From the study, it turns out that the differences between science career choosers and non-science career choosers lie in the following factors: 1) "family", 2) "school success", 3) "school science", 4) "out-of-school science", and 4) "personality". Al l these factors encourage physical-science and biological-science career choosers to select science and engineering related careers. Therefore, these are important factors that contribute to students' career selection. The differences between male and female students lie in the following factors like 1) "family", 2) "school success", and 3) "personality". From this study, we cannot tell which are the most important factors affecting students' choice; however, we can at least say that these are very important factors for all these factors have been regarded as important in the previous research. Although findings from other studies such as "student-centered science activity was preferred by physical science choosers" and "teacher-centered was preferred by non-science choosers" were not confirmed in this study, all types of students in the study had similar positive attitudes toward "teacher-centered activity". One reason for this result might be because of students' lack of relevant experience of "student-centered activity" in school. It seems from this study that students' avoidance of science in Canada starts in high school when they select courses. Many students lack the opportunity to consider science because they lack a high school mathematics and science background. 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(1988). The career goals of female science students in Canada. Canadian Journal of Higher Education. 18 (1). 31-48. 94 Ormerod, M . B . & Duckworth, D. (1975). Pupils' attitudes to science: a review of research. E R I C Document Reproduction Service No. E D 118372. Gaskell, P. J, Mclaren, A . , Oberg, A . & Eyre, L . (1993). The 1990 British Columbia Mathematics Assessment: Gender Issues in Student Choices in Mathematics and Science. Reyes, L . H . & Padilla, M . J. (1985). Science, math and gender. T h e Science Teacher. 52 (6). 46-48. Rossi, A . S. (1965), Women in science, why so few? Science. 148. 1196-1201. Seymour, E . (1992). "The problem iceberg" in science, mathematics, and engineering education: student explanations for high attrition rates. Journal of College Science Teaching. 21 (4), 230-238. Shannon, A . G . , Sleet, R. L . & Stern, W. W.(1982). School students' attitudes to science subjects. Journal of Research in Science Teaching. 28. 77-82. Sjoberg, S., B . I. (1988). Gender and science education: 1. Development and dilemmas in science education. New York: Falmer Press. Super, A . (1953). A theory of vocational development. A m e r i c a n Psychology. 8. 185-190. Taber, K . S. (1992). Science-relatedness and gender-appropriateness of careers: some pupil perceptions. Research in Science and Technology education. 10 (1). 105-115. Thomas, G . E . (1986). Cultivating the interest of women and minorities in high school mathematics and science. Science Education. 70 (1), 31-43. Thomas, P. D . & Rallis, S. F . (1991). Factors and influences on high school students' career choices. Journal for Research in Mathematics Education. 22 (4), 281-292. 95 Tittle, C . K . (1981). Sex differences in occupational values: Implications for reducing sex bias. E R I C Documentary Reproduction Service No. E D 209625. Tobin, K . & Garnett, P. (1987). Gender related differences in science activities. Science Education. 71. 91-103. Ventura, F . (1992). Gender, science choice and achievement: a maltese perspective. School Science Review. 74. 445-461. Ware, N . C . & Lee, V . E . (1988). Sex differences in choice of college science major. Research in Science and Technology Education. 25 (4), 593-614. Ware, N . C , Steckler, N . A . , & Leserman, J . (1985). Undergraduate women: who chooses a science major? Journal of Higher Education: 56 (1), 73-84. Welch, W . W . (1985). The research in science education: review and recommendations. Science Education. 69. 421-448. Wells, R. L . & Gaus, D . (1991). Study of kentucky middle school students' knowledge of career options. E R I C Documentary Reproduction Service No. E D 351515. Wiener, M . J. (1981). English culture and the decline of the industrial spirit 1850-1980. Cambridge: Cambridge University Press. Woolnough, B . E . (1990). Making choices: an inquiry into the attitudes of sixth-formers towards choice of science and technology courses in higher education. Oxford: Oxford University Department of Educational Studies. Woolnough, B . E . (1991). The making of engineers and scientists. Oxford, Oxford University Department of Educational Studies. Woolnough, B . E . (1994). Factors affecting students' choice of science and engineering. International Journal of Science Education (in press). 96 APPENDIX A Dear Principal: We are writing to request permission to conduct a research project titled "The Making of Engineers and Scientists" in your school. The project has been reviewed by the Board office and we have received permission from him to approach you. The research will consist of asking 20 Grade 12 students in your school to fill out a copy of the attached questionnaire, a modification of one developed at Oxford University in England. This project is part of an international study designed to investigate the importance of different factors which may influence students in deciding whether or not to choose careers in science or engineering. The results of this study will help us to know which factors are important to Canadian students' career choice in science or engineering as well as their attitudes toward science in school. Approximately 20 schools and 400 students in British Columbia are being asked to participate. Your school was randomly selected from the Abbotsford School District. The questionnaire will take approximately 15 minutes for the students to complete. If you agree to participate, you may want to use the following procedure although any procedure that produces a set of 20 students randomly drawn from the Grade 12 population is acceptable. If you would like to send a complete list of Grade 12 students to us, we would be happy to randomly choose students for you. 97 Suggested Procedure 1. Selection of 25 Random Grade 12 Students (It is useful to begin with 25 even though only 20 are needed on the assumption that some students may decline to participate or be away from school.) a) Create a list of all Grade 12 students. b) Divide the total number of Grade 12 students by 25--this will produce a number "n". (e.g. If you have 280 students in Grade 12, n=l l . ) c) Choose a number randomly between 1 and "n" — this will give you a second number "a", (e.g. 6) d) Count down from the top of the list of students until you come to the number "a". This will be your first student. e) Select every "nth" student after the first student. 2. Distribution of Questionnaire Ask a school counsellor to contact each of the students selected by the above procedure and request that they come to the counselling office at a time convenient to them to fill out the questionnaire. In this way, no instructional time will be used by the students for this project. Alternatively, the counsellor may call all the students together at one time during lunchtime or other convenient time. O f course, as is clear from the instructions to the students, this is a voluntary exercise and students may wish to decline the invitation. 99 A P P E N D I X B STUDENT QUESTIONNAIRE (Anonymous) SECTION A: STUDENT INFORMATION FORM 1. Please note all the subjects you are studying, or have studied in Grade 12. SUBJECT LEVEL 2. Please note which subjects you took in Grade 11, and the marks or grades you obtained in them. SUBJECT LEVEL MARK/GRADE 3. Please tick your gender. Di Female • Male 100 4. D i d your father study science or engineering in college or • Yes • No • I Don't know Does he work in a science or technology area? • Yes • No • I Don't know If yes, please specify:_ 5. D id your mother study science or engineering in college or university? • Yes • No • I Don't know Does she work in a science or technology area? • Yes • No • I Don't know If yes, please specify: 6. Have you decided on a career yet? • Yes Please note your career choice: • No If no, think about the question, then select the first career that comes into your mind. Career choice: 7. Please note whether you hope to go to post-secondary education: • Yes • No If yes, what fi61d do you hope to specialize in? Please specify: 8. In what grade did you make your decision towards, or away from, a career in science or engineering? CU Still undecided [Z • In Grade 8 or Grade 9 C In Grade 12 • In Grade 10 or In elementary school Grade 11 9. Which one phrase best describes the reasons for choosing your Grade 11 or Grade 12 options? (Please tick only one) D Attracted by subjects chosen D Put off by subjects not taken • Pre-requisite for entrance to D Attracted by the teacher teaching the course D Other reasons, please specify: -university or college IZI No strong reasons Further comments on the reasons for choosing Grade 12 options: 101 SECTION B: ACrrVITY IN SCHOOL SCIENCE In this section we would like you to record how you feel about various statements about science activities in school. Please tick or place an X in the box to indicate whether you "strongly agree", "agree", are "neutral", "disagree", or "strongly disagree" with the following statements. If you feel that you cannot make the judgment, please mark the " N A " (not applicable) box. 10. I find the opportunity to plan my own experiments very satisfying. 11. Student work should be marked objectively by the teacher. 12. School science should be about learning scientific facts and theories. 13. I value the opportunity when the teacher lets us plan our own activities in science courses. 14. I feel most confident when the science courses are highly structured and teacher directed. 15. Involvement in science clubs is helpful in the learning of real science. 16. A n extended practical project shows me what science is like and gets me interested in it. 17. I feel happiest when clear, step by step instructions are given when doing labs. 18. Involvement in science and technology competitions is great fun and useful. 19. The most effective form of assessment is self-assessment by the student. Strongly Agree Agree Neutral Disagree Strongly Disagree |NA \ 102 Strongly Agree Agree Neutral Disagree Strcngly Disagree 20. School science should be about learning to do science through scientific investigations. 21. Standard experiments, written up correctly, give me confidence to continue with science. 22. The best notes for me to write are short and concise. 23. Work experience in science based industry turns people off jobs in science or engineering. 24. Parents should not be involved in the work of the school science department. 25. The times when the school suspends its normal time table for extended projects are not very useful. 26. I find I need to write quite a lot to really express myself clearly. 27. Local scientists and engineers can bring a stimulating dimension into science courses. 28. Science helps me to understand things in everyday life. 29. Science involves lots of mathematics. 30. Science courses are more difficult than courses in languages or social studies. 31. Students in science courses are very aggressive and competitive. " 103 SECTION C: FACTORS INFLUENCING CAREER CHOICE In this section, please think about the factors that have influenced or might influence your career choice in one of the physical sciences or engineering. If the factor has a positive effect in encouraging you to study one of the physical sciences or engineering, please tick or place an X in the "Positive", or the "Very Positive" box. If it has the effect of discouraging you from continuing with the sciences or going into engineering, please tick or place an X in the "Negative", or "Very Negative" box. If the factor has no significant effect either way, please tick or place an X in the "Neutral" box. If the factor described below is not applicable in your case, please mark the "NA" box. Very Positive Positive Neutral ^gative Very Negative 32. Your intellectual satisfaction in doing science. 33. Your exam results in science. 34. Your exam results in mathematics. 35. Your practical ability in doing science. 36. The quality of science courses. 37. The level of difficulty of the science at school. 38. The quality of technology courses. 39. The amount of involvement with human issues in science courses. 40. The quality of mathematics courses. 41. The amount of work required for school science. 42. The practical nature of science courses. 43. The amount of self-expression allowed in science courses. 44. Advice from the school counselor. 45. The personal encouragement given by science teachers. 104 46. The possibility of funding to study science or engineering in college or universi ty. 47. Involvement in science competitions. 48. Local scientists and engineers coming into the school. 49. Visits to science firms. 50. Job satisfaction in a career in science and engineering. 51. Father's occupation. 52. Scientific hobbies or tinkering with gadgets at home. 53. Parental encouragement. 54. Experiences of your family in science related industry. 55. Mother's occupation. 56. The social status of jobs in science and engineering. 57. The salaries offered in science and engineering jobs. 58. Work experience in local companies. 59. The availability of jobs in science and engineering. 60. Involvement in science clubs. 61. The ease of entry to college or university in science and engineering. 62. The balance between career and family obligations. 63. The situation in local science based industry. 64. The sophisticated technology used in military weapons. 65. Other factor, please specify: Very Positive Positive Neutral Negative Very Negative 105 SECTION D: HOW WOULD YOU DESCRIBE YOURSELF Please indicate by the placement of a tick at the appropriate space on each line where you think you best fit on the characteristic defined by the words at each end of that line. The central space indicates that you think neither word applies to you, or they both apply equally. The more strongly you think one of the words applies to you, the further from the centre you should place the tick. I would describe myself as: hard working clever introverted self-confident task-centered verbose tender-minded abstract thinker interested in people creative convergent thinker gregarious communicating best in words dominant lazy stupid extroverted insecure person-centered concise tough-minded practical worker interested in ideas systematic divergent thinker a loner communicating best in diagrams submissive 106 J casual J timid dependent on others J generous J sober conscientious adventurous self-sufficient mercenary enthusiastic 107 SECTION E: ADDITIONAL COMMENTS Thank you very much for completing the previous sections. If you would like to write anything more about how you decided to, or decided not to, pursue a career in science or technology, please feel free to do so in the space below. I would be very interested in your comments. Thank you for completing this questionnaire. Please place it in the envelope provided, seal it and return it to the teacher or guidance counselor. 108 APPENDIX C non- sc ience p h y s i c a l - sc ience b io log ica l - science STUDENT T Y P E related chooser related chooser related c hooser male female male female male female n u m b e r 67 88 54 12 3 5 36 means of the 1.96 1.47 3.02 4.0.9 3.52 3.22 number of pre-requisi te science course selection mean of course 4.56 4.87 4.96 5.36 5.09 5.17 grade (student ab i l i t y ) mean of math 4.00 4.42 4.90 5.38 4.99 4.83 and science course grade % whose father 20.9 28.4 32.7 63.6 35.3 21.6 has science-related higher educa t i on % whose father 19.4 23.3 38.9 „ 63.6 20.6 22.2 has science-related job % whose mother 16.7 13.6 10.9 45.5 12.1 18.9 has science-r e l a t e d educa t i on % whose mother 6.0 6.8 12.7 27.3 9.1 25.0 has science-related job % career 58.2 68.2 65.5 36.4 52.9 62.2 decis ion made % decision to 95.4 95.5 98.2 100 100 100 p o s t - s e c o n d a r y educa t i on 109 % of time to make decision still undecided 19.4 11.4 7.3 0 11.8 11.1 in Grade 12 19.4 17.0 23.6 45.5 17.6 11.1 in Grade 10 or 40.3 44.3 47.3 36.4 35.3 47.2 1 1 11.9 17.0 12.7 9.1 17.6 16.7 in Grade 8 or 9 9.0 10.2 9.1 9.1 17.6 13.9 in elemenary reasons for course selection 41.3 48.9 52.7 27.3 23.5 44.4 affected by subjects chosen 4.8 5.7 0 0 2.9 5.6 put off by subjects not 25.4 23.9 34.5 63.6 47.1 44.4 taken pre-requisite for i entrance to •} un ivers i ty 3.2 21.6 0 0 5.9 0 attracted by the teacher 22.2 0 9.1 0 8.8 5.6 no strong 3.2 0 3.6 9.1 11.8 0 reasons other reasons 110 APPENDIX D (FIGURE 1) SPSS for Unix - U B C UCS Analysis Number 1 LISTWISE DELETION OF CASES WITH MISSING V A L U E S CORRELATION MATRIX (OMMITTED) KISER-MEYER-OLKIN M E A S U R E OF SAMPLING ADEQUACY=.75567 B A R T L E T T TEST OF SPHERICITY = 1152.9911, SIGNIFICANCE=.00000 A P P E N D I X D ( F I G U R E 2) SPSS FOR UNIX - UBC UCS ANALYSIS NUMBER 1 LISTWISE DELETION OF CASES WITH MISSING VALUES EXTRACTION 1 FOR ANALYSIS 1, PRINCIPAL-COMPONENTS ANALYSIS (PC) INITIAL STATISTICS: VARIABLE COMMUNALITY FACTOR EIGEN PCTOF CUM VALUE VAR per Sl 1.00000 1 4.04293 18.4 18.4 S2 1.00000 2 1.94914 8.9 27.2 S3 1.00000 3 1.51330 6.9 34.1 S4 1.00000 4 1.34260 6.1 40.2 S5 1.00000 5 1.20488 : 5.5 45.7 S6 1.00000 6 1.17270 5.3 51.0 S7 1.00000 7 1.03109 4.7 55.7 S8 1.00000 8 .93398 4.2 60.0 S9 1.00000 9 .88133 4.0 64.0 S10 1.00000 10 .84976 3.9 67.8 S l l 1.00000 1 1 .80791 3.7 71.5 S12 1.00000 1 2 .77356 3.5 75.0 S13 1.00000 1 3 .71909 3.3 78.3 S14 1.00000 1 4 .70908 3.2 81.5 S15 1.00000 15 .63646 2.9 84.4 S16 1.00000 1 6 .62601 2.8 87.2 S17 1.00000 1 7 .58070 2.6 89.9 S18 1.00000 1 8 .56953 2.6 92.5 S19 1.00000 1 9 .50067 2.3 94.7 S20 1.00000 20 .41456 1.9 96.6 S21 1.00000 21 .37660 1.7 98.3 S22 1.00000 22 .36411 1.7 100.0 112 APPENDIX D (FIGURE 3) VAPJMAX ROTATION 1 FOR EXTRACTION 1 IN ANALYSIS 1 - KAISER NORMALIZATION ROTATED FACTOR MATRIX: FACTOR 1 FACTOR2 FACTOR3 FACTOR4 FACTOR5 FACTOR 6 FACTOR7 57 .70285 51 .64271 S9 .63311 S6 .59941 519 .55711 54 .55230 S18 .53032 512 .51813 S l l .42223 52 .68595 53 .57561 55 .57043 520 .72804 S22 .70531 516 .76238 514 .52857 515 .46913 513 .79095 517 .77575 521 . .61274 58 .79114 S10 -40901 113 APPENDIX E (FIGURE 1) SPSS FOR UNIX -- UBC UCS CORRELATION MATRIX (OMMITED) KAISER-MEYER-OLKIN MEASURE OF SAMPLING ADEQUACY=.81504 BARTLETT TEST OF SPHERICITY = 2575.0741, SIGNIFICANCE=.00000 114 APPENDIX E (FIGURE 2) SPSS FOR UNIX--UBC UCS ANALYSIS NUMBER 1 PAIR WISE DELETION OF CASES WITH MISSING VALUES EXTRACTION 1 FOR ANALYSIS 1, PRINCIPAL-COMPONENTS ANALYSIS (PC) INITIAL STATISTICS: VARIABLE COMMUNALITY FACTOR EIGEN PCTOF CUM VALUE VAR PCT CI 1.00000 1 8.27642 25.1 25.1 C2 1.00000 2 3.71902 11.3 36.3 C3 1.00000 3 2.18751 6.6 43.0 C4 1.00000 4 1.54741 4.7 47.7 C5 1.00000 5 1.38905 4.2 51.9 C6 1.00000 6 1.19064 3.6 55.5 C7 1.00000 7 1.12398 3.4 58.9 C8 1.00000 s. 1.06389 13, 62.1 C9 1.00000 9 .99837 3.0 65.1 CIO 1.00000 10 .89299 2.7 67.8 C l l 1.00000 11 .86034 2.6 70.5 C12 1.00000 12 .75549 2.3 72.7 C13 1.00000 13 .73590 2.2 75.0 C14 1.00000 14 .72256 2.2 75.0 C15 1.00000 15 .67447 2.0 79.2 C16 1.00000 16 .62439 1.9 81.1 C17 1.00000 .17 . .61488 1.9 83.0 C18 1.00000 18 .57156 1.7 84.7 C19 1.00000 19 .52293 1.6 86.3 C20 1.00000 20 .49627 1.5 87.8 C21 1.00000 21 .45720 1.4 89.2 C22 1.00000 22 .45523 1.4 90.5 C23 1.00000 23 .43720 1.3 91.5 C24 1.00000 24 .41575 1.3 93.1 C25 1.00000 25 .35477 1.1 94.2 C26 1.00000 26 .33668 1.0 95.2 C27 1.00000 27 .33035 1.0 96.2 C28 1.00000 28 .26773 .8 97.0 C29 1.00000 29 .24191 .7 97.8 C30 1.00000 30 .22821 .7 _ 98.5 C31 1.00000 31 .19565 .6 99.1 C32 1.00000 32 .18643 .6 99.6 C33 1.00000 33 .12482 .4 100.0 115 APPENDIX E (FIGURE 3) VARIMAX ROTATION 1 FOR EXTRACTION 1 IN ANALYSIS 1 - KAISER NORMALIZATION ROTATED FACTOR MATRIX: FACTOR 1 FACTOR2 FACTOR3 FACTOR4 FACTOR5 FACTOR6 FACTOR7 C25 .77273 C27 .75053 C28 .72917 C26 .70444 C29 .55062 C24 .82705 C20 .80390 C23 .72302 C31 .64682 C21 .42651 C3 .77747 C2 .76349 CI .60730 C19 .46468 .42671 C15 .45419 C10 .73869 C12 .70671 C l l .64437 C14 .49315 C7 .68229 C5 .63182 C4 .46885 .59358 C8 .42276 .56499 C6 .49765 C17 .80471 C18 .56500 .65055 C16 .56163 C32 .52590 C33 .55081 C13 .53802 C22 .49384 -.50346 116 APPENDIX F (FIGURE 1) SPSS FOR UNIX - UBC UCS CORRELATION MATRIX (OMMITED) KAISER-MEYER-OLKIN MEASURE OF SAMPLING ADEQUACY=.73940 BARTLETT TEST OF SPHERICITY = 2575.0741, SIGNIFICANCE=.00000 117 APPENDIX F (FIGURE 2) SPSS F O R U N I X - U B C U C S A N A L Y S I S N U M B E R 1 PAIR W I S E D E L E T I O N O F C A S E S W I T H MISS ING V A L U E S E X T R A C T I O N 1 F O R A N A L Y S I S 1, P R I N C I P A L - C O M P O N E N T S A N A L Y S I S (PC) IN IT IAL S T A T I S T I C S : L I A B L E C O M M U N A L I T Y F A C T O R E I G E N P C T O F C U M V A L U E V A R P C T PI 1.00000 1 3.68002 19.4 19.4 P2 1.00000 2 2.20967 11.6 31.0 P3 1.00000 3 1.76809 9.3 40.3 P4 1.00000 4 1.52535 8.0 48.3 P5 1.00000 . 5 ' 1.16477 .6.1 54.5 P6 1.00000 6 1.07460 5.7 60.1 P7 .1.00000 7 .85177 4.5 64.6 P8 1.00000 8 .79776 4.2 68.8 P9 1.00000 9 .73318 3.9 72.7 P10 1.00000 10 .69381 3.7 76.3 P l l 1.00000 11 .66712 3.5 79.8 P12 1.00000 12 .64936 3.4 83.2 P13 1.00000 13 .59815 3.1 86.4 P14 1.00000 14 .54032 2.8 89.2 P15 1.00000 15 .47371 2.5 91.7 P16 1.00000 16 .44988 2.4 94.1 P17 1.00000 17 .40030 2.1 96.2 PI 8 1.00000 18 .37752 2.0 98.2 P19 1.00000 19 .34364 1.8 100.0 118 APPENDIX F (FIGURE 3) Q U A R T I M A X R O T A T I O N 1 F O R E X T R A C T I O N 1 I N A N A L Y S I S 1 -K A I S E R N O R M A L I Z A T I O N R O T A T E D F A C T O R M A T R I X : FACTOR 1 FACTOR2 FACTOR3 FACTOR4 FACTOR5 FACTOR 6 P4 .75932 P14 \73786 P17 .67668 P16 .58064 P2 .52255 P19 .45592 .42046 P3 -.44544 P9 .74217 P13 .67596 P12 .55399 P7 P15 PI P5 71462 66454 58125 P8 P10 .82363 .76048 P18 P6 .44216 .77153 .53564 P l l .87587 

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